Blockchain cellular system

ABSTRACT

A system includes a distributed ledger storing one or more smart contracts; one or more 5G small cells, each having one or more antennas mounted on a housing, each small cell sending packets of data trackable with the distributed ledger; and a processor to control a directionality of the antennas in communication with a predetermined target using 5G protocols.

The present invention relates to cellular systems.

2G, 3G and 4G cellular wireless technologies have been mass deployedthroughout the world. Moreover personal area network based technologiessuch as wifi, bluetooth and zigbee have become predominant in our dailylife. 5G is the short form of 5th Generation. It is used to designatefifth generation of mobile technologies. 5G has made it possible to usemobile phone with larger bandwidth possible. It is a packet switchedwireless system. It is used to cover wide area and used to providehigher throughput. It uses CDMA, BDMA and also millimeter wave (forbackhaul wireless connectivity). It uses improved and advanced datacoding/modulation techniques. It provides about 100 Mbps at fullmobility and 1 Gbps at low mobility. It uses smart antenna techniques tosupport higher data rate and coverage.

5G cell phones use radio frequencies in various bands as per countrywise allocations. Typically it uses less than 1 GHz, below 6 GHz andabove 6 GHz (i.e. mmwave) frequency bands. It delivers fastuplink/downlink throughput due to massive MIMO and lower latency between5G network (i.e. SGNB) and itself. The 5G cell phone supports 10 timesthroughput compare to 4G phones. They are backward compatible to 4Gstandards such as LTE and LTE-advanced. Moreover latest 5G phones willsupport bluetooth, wifi and NFC based short distance wirelesstechnologies. GPS is also incorporated to support various GPS basedapplications including location tracking, google maps etc.

5G promises an extremely interconnected world where everything fromsmartwatches, vehicles, houses, and farms utilize the ultrafast speedsand low delays it offers. To accomplish this, and to do it well—with aslittle coverage gaps as possible—it's required to have a huge number of5G towers, particularly in areas that demand lots of traffic like bigcities and business districts. Another reason 5G towers have to beinstalled so frequently in busy areas is because for the small cell tosupport superfast speeds, it has to have a direct line of sight with thereceiving device. Since 5G cell towers are so small, they can bepositioned in ordinary places like on light poles, the tops ofbuildings, and even street lights. This translates into less traditionallooking towers but also potentially more eyesores nearly everywhere.

5G relies on massive multi-antenna (MIMO) where NT transmitting antennasare provided to a transmitting stage, while NR receiving antennas areprovided to a receiving stage. The increase of the channel transmissioncapacity is in proportion to the number of antennas, assuming that thetransmitter in a wireless communication system knows the channel. Forchannel estimation without interference, the RSs of multipletransmitters should be orthogonal to each other. If there is acorrelation between the RS from the first transmitter to the firstreceiver and the RS from the second transmitter to the second receiver,the channel estimation at the first receiver may reflect not only thechannel from the first transmitter to the first receiver but also thechannel from the second transmitter to the first receiver. It can besaid that the channel from the first transmitter to the first receiveris contaminated by the channel from the second transmitter to the firstreceiver (pilot contamination).

SUMMARY

A system includes a distributed ledger storing one or more smartcontracts; one or more 5G small cells, each having one or more antennasmounted on a housing, each small cell sending packets of data trackablewith the distributed ledger; and a processor to control a directionalityof the antennas in communication with a predetermined target using 5Gprotocols.

The system leverages blockchain's Smart Contract Integration. Each host(home/business/location) with a small cell (such as a femto cell) iscalled a hotspot host. When the hotspot host turns on a 5G small cellthat members can access, they are paid a fee via a smart contract.Hotspot Hosts may also be rewarded for viewing push messages fromadvertisers. The hosts elect how many and what type of message theywould like to view. The advertiser is therefore aware of who will viewhis messages, when and how frequently. This is a smart contract with 3players, where the system provisions reward for a member or hostwatching the message, the advertiser provisions reward for us providingthe service, when the message is watched the smart contract completesand rewards are distributed.

In one implementation, a user downloads an app for mobile devices ororders a pre-configured router. The app will activate the mobile hotspotfunction on the Hotspot Host's smart device and wireless access will bemade available to network members in the vicinity. The Hotspot Hostsonly need to make the data available for one hour each day to satisfythe smart contract and trigger payment. Payments are made on a dailybasis. Hosts benefit from using the 5G small cell routers to createsecure Wi-Fi/5G hotspots for the general public. Non hosting members canlog in as a pay account (ad-free) or a free account with ads. For admembers, the system collects Name, Email Address, Social Media(Facebook/Linkedin) Profiles, Email/Marketing “opt-in.”

Other inventive aspects are disclosed below:

LIQUID LENS ANTENNA with a liquid lens with moveable surface, whereinliquid is added or removed to adjust the curvature of the movablesurface; and an antenna mounted on the moveable surface to change adirection of the antenna to a predetermined target.

STEERABLE ACTUATED ANTENNA with a moveable surface; and one or moreantennas mounted on the moveable surface to change a direction of theantenna to a predetermined target.

LEARNING SYSTEM PLANE to optimize data flow in a 5G network with aneural network plane; a control plane coupled to the neural networkplane; a management plane coupled to the neural network plane; a dataplane coupled to the neural network plane, wherein the neural networkplane receives cellular network statistics from the data plane fortraining, and during run-time, the neural network provides operatingparameters to the data, control and management planes; and one or moreoperations sending resource request to the neural network plane forautonomous resolution that maximizes data flow in the system.

CITY LIGHT OR STREET LIGHT ANTENNA with a city light or a street lightmounted above a pole, the city light having a housing; and one or moreantennas mounted on the housing and in communication with apredetermined target using 5G protocols.

3G/4G CELL TOWERS with a cell tower with a pole and a top portion tomount 4G antennas and a 5G housing; and one or more mechanicallysteerable active antennas mounted on the 5G housing and in communicationwith a predetermined target using 5G protocols.

ACTUATOR-BASED ACTIVE ANTENNA ARRAY with an array of antenna element,each connected to a separate transceiver; an array of actuators to pointthe antenna elements; data converters coupled to the transceivers for upconversion and down conversion; a baseband unit (BBU) with one or moredigital signal processors coupled to the data converters; and abroadband connection connecting the baseband unit to a wide area network(WAN).

The system may include one or more of the following:

BEAMFORMING ACTUATOR DRIVEN ACTIVE ANTENNA TO TRACK MOVING UEs with amethod of communicating data with a UE using an array antenna onboard acell tower and having a digital beam former (DBF), said array antennahaving a plurality of actuators moving the RF radiating elements forproviding steerable antenna beams within an antenna footprint region,said DBF providing for each radiating element, beam forming coefficientsfor controlling characteristics of said steerable antenna beams. Otherimplementations include receiving a signal from the UE within a receiveone of said steerable antenna beams; determining a location direction ofthe UE using said signal; generating digital beam forming coefficientsto provide a transmit one of said steerable antenna beams in saidlocation direction of the UE; transmitting data from said cell tower tosaid UE within said one transmit steerable antenna beam; tracking saidlocation direction of said UI as said cell tower and said UE moverelative to each other; adjusting said beam forming coefficientsassociated with one transmit steerable antenna beam in response to thetracking step to maintain said one transmit steerable antenna beam inthe location direction of said UE; further adjusting said beam formingcoefficients associated with one transmit steerable antenna beam toimprove a signal quality of communication signal received at saidcommunication station.

MULTI-LEVEL 5G/6G ANTENNA with a high power active antenna array mountedon a cell tower, balloon, or a drone, the high power active antennaarray controlled by a BBU with a broadband connection; and a pluralityof medium power active antenna arrays wirelessly coupled to the highpower active antenna, wherein the medium power antenna array relays datatransmission between the high power active antenna array and a UE toreduce RF exposure on biologics. This reduces cancer risk on users.

CAR/TRUCK/VAN/BUS/VEHICLE WITH 5G ANTENNA SMALL CELLS with a moveablevehicle including a pole and a top portion to mount 4G antennas and a 5Ghousing, wherein the pole is retractable and extendable during 5Goperation; one or more antennas mounted on the 5G housing and incommunication with a predetermined target using 5G protocols.

GLIDER/HELICOPTER/BALLOON/SHIP/LOW EARTH ORBIT DRONE WITH 5G ANTENNAwith an airborne frame to mount 4G antennas and a 5G housing; one ormore antennas mounted on the 5G housing and in communication with apredetermined target using 5G protocols.

CELL PHONE ANTENNA with a cell phone housing; and one or more antennasmounted on the housing, the antenna being selectable to avoiddischarging RF energy into a human body and to target RF energy at apredetermined target.

CELL PHONE BODY WITH MOVABLE ANTENNA with a cell phone housing having amoveable surface; and one or more antennas mounted on a moveablesurface, wherein the antenna direction is changed by the moveablesurface to target RF energy at a predetermined target.

CELL PHONE LIQUID METAL ANTENNA with a cell phone housing; a pluralityof channels on the housing; and one or more liquid antenna movable onthe channels to change a frequency or a direction of the antenna to apredetermined target.

CANCER MINIMIZATION OF 5G CELL PHONES with a 5G transceiver spaced apartfrom a user to minimize 5G radiation directly on the user body; and adisplay and microphone/speaker coupled to the 5G transceiver which isnearer to the user body than the 5G transceiver.

CANCER MINIMIZATION OF 5G VEHICLES with a 5G transceiver to receive 5Gtransmission; a faraday cage isolating the user from the 5G transceiver;and a display and microphone/speaker in the faraday cage and incommunication with the 5G transceiver which is nearer to the user bodythan the 5G transceiver.

POWERING OF IOT DEVICES USING 5G ENERGY with a housing having a moveablesurface; one or more antennas mounted on a moveable surface, wherein theantenna direction is changed by the moveable surface to receive RFenergy from a small cell; a capacitor, battery or energy storage devicecoupled to the antennas to store received energy; and a power regulatorcoupled to the capacitor, battery, or energy storage device to power theIOT system.

ANTENNA WITH EVAPORATIVE COOLING FOR 5G POWER AMPLIFIERS with anenhanced boiling or evaporation microstructure surface includingmicroporous structures; and an electro-deposited surface to enhance avapor condensation rate, wherein the surface includes a porous medium toreplenish condensed liquid back to the microstructure surface bycapillary pumping force, wherein the surface is part of an antenna.

LOW ORBIT DRONE WITH ACTIVE ANTENNAS with an airborne frame to mount 4Gantennas and a 5G housing; a variable buoyancy propulsion with acombination of a lighter than air chamber and a compressed gas chamberto propel the airborne frame; and one or more antennas mounted on the 5Ghousing and in communication with a predetermined target using 5Gprotocols.

HYDROGEN REFUELING DRONE with a moving body including a hydrogen tank ata high pressure; sensors to determine current positions of the refuelingdrone and the target vehicle; sensors on the drone and target vehicle todetermine hydrogen fuel parameters; navigation processor to control themoving body to a predetermined distance near the target vehicle; a probeextending from the moving body to a refill receptacle on the targetvehicle, wherein the processor extends the probe from the moving body toenter the target vehicle receptacle at a lower pressure; and a valveopened to release hydrogen from the hydrogen tank to a fuel container inthe target vehicle at a lower pressure than the high pressure at thehydrogen tank.

More details are disclosed in co-pending application Ser. No.16/404,853, the content of which is incorporated by reference.

Each of the above aspect or system may include one or more of thefollowing:

2. A viscous liquid in the lens can be injected under processor controlto change the curvature of the lens and to change the directionality ofthe antenna.

3. The processor can calibrate the RF link between the tower and theclient device.

4. The processor can calibrate the connection by examining the RSSI andTSSI and scan the moveable lens until the optimal RSSI/TSSI levels (orother cellular parameters) are reached.

5. The scanning of the lens can be done by injecting or removing liquidfrom the lens.

6. Opposing pairs of lenses can be formed to provide two-sidedcommunication antennas.

7. An array of liquid lens can be used (similar to bee eyes), eachantenna is independently steerable to optimize 5G transmission.

8. Fresnel lens can be used to improve SNR.

9. The focusing of the lens can be automatically done using processorwith iterative changes in the orientation of the antenna by changing thelens shape until predetermined criteria is achieved such as the besttransmission speed, TSSI, RSSI, SNR, among others. This is similar tothe way human vision eyeglass correction is done.

10. A learning machine such as neural network or SVM can be used overthe control/management plane of the 5G network to optimize 5G parametersbased on local behaviors.

11. A learning machine such as neural network or SVM can be used overthe control/management plane of the 5G network to optimize 5G parametersbased on local behaviors. The learning machine can be used to helpsteering the antennas to improve connections with UEs. The learningmachine can also optimize operation based on data collected from otherelements in the transceiver and/or the BBU. The broadband connection canbe fiber optic or wireless connection (UWB). The baseband unit can havea high-speed serial link as defined by the Common Public Radio Interface(CPRI), Open Base Station Architecture Initiative (OBSAI), or Open RadioInterface (ORI). The high speed serial link is used to transport the Txand Rx signals from the BBU to the antennas. The AAS can have passivecooling fins on the housing, or can use evaporative cooling techniques,for example with an enhanced boiling or evaporation microstructuresurface including microporous structures; and an electro-depositedsurface to enhance a vapor condensation rate, wherein the surfaceincludes a porous medium to replenish condensed liquid back to themicrostructure surface by capillary pumping force, wherein the surfaceis part of an antenna. Since there are many more transceivers/amplifiersin an AAS, each amplifier in an AAS delivers a much lower power whencompared to an amplifier in an equivalent RRH.

12. Cameras and sensors can be positioned to capture securityinformation.

13. Learning machine hardware can provide local processing at the edge.

14. The air frame has an antenna support structure having means topermit its collapsing and a waveguide antenna mounted to said supportstructure and including a plurality of integrally connected tubularwaveguide cells that form a cell array that focuses transmitted signalsonto a signal processing device; said lens waveguide antenna havingmeans to permit its collapsing and a second support structure mount thatoperatively connects said collapsible support structure to a mountingsurface to correctly position said collapsible lens waveguide antennarelative to said signal processing device when said antenna isoperationally deployed.

15. A fleet of drones can operate and navigate as a flock of birds toprovide real time adjustment in coverage as needed. The flock of birdsantenna has power and autonomous navigation and can self-assemble andscatter as needed to avoid physical and wireless communicationobstacles.

16. A refueling drone can be used to supply the GBS with power by swapbattery with the GBS or refueling the hydrogen fuel cells, where therefueling drone designed for boom-type transfers in which a boomcontroller extends and maneuvers a boom to establish a connection totransfer hydrogen fuel from the refueling drone to the refueling drone.Prior to refueling, the refueling drone extends a refueling probe.

17. The refueling drone includes a navigation system that may be usedfor positioning the refueling drone during aerial refueling. The GBSnavigation system provides inertial and Global Positioning System (GPS)measurement data to the refueling drone via a data link. Relativepositioning can be used to navigate both crafts.

BRIEF DESCRIPTION OF THE DRAWINGS

FIGS. 1A-1E show an exemplary 5G network architecture, while FIGS. 1F-1Gshow exemplary 5G mobile devices.

FIGS. 2A-2B show an exemplary city light small cell environment withcrime/pollution sniffing capabilities.

FIG. 2C shows a ground based, light based, and plant based antennanetwork.

FIG. 2D shows an exemplary security camera with small cell and antennas.

FIG. 2E shows an exemplary blockchain enabled 5G network.

FIG. 2F shows an exemplary 4G-5G network in accordance with one aspect.

FIG. 2G shows vehicles for 5G operations.

FIG. 2H-2I show exemplary MIMO systems.

FIG. 2J shows exemplary mobile 5G towers or mobile 5G small cells.

FIG. 3 shows an exemplary neural network controlled MIMO systems.

FIGS. 4A-4C show exemplary learning machine processes and architectures.

DESCRIPTION

FIGS. 1A-1D shows an exemplary 5G network architecture. A plurality ofphones running 2G, 3G, 4G and 5G communication with wireless RANs. Theradio access network (RAN) has been in use since the beginning ofcellular technology and has evolved through the generations of mobilecommunications (1G, 2G, 3G, 4G, and in anticipation of the forthcoming5G). Components of the RAN include a base station and antennas thatcover a given region depending on their capacity. In a RAN, radio sitesprovide radio access and coordinate management of resources across theradio sites. A device is wirelessly connected to the core network, andthe RAN transmits its signal to various wireless endpoints, and thesignal travels with other networks' traffic. Two types of radio accessnetworks are Generic Radio Access Network (GRAN), which uses basetransmission stations and controllers to manage radio links forcircuit-switched and packet-switched core networks; and GSM Edge RadioAccess Network (GERAN), which supports real-time packet data. Two othertypes of radio access networks are UMTS Terrestrial Radio Access Network(UTRAN), which supports both circuit-switched and packet-switchedservices; and Evolved Universal Terrestrial Radio Access Network(E-UTRAN), which focuses only on packet-switched services. E-UTRAN alsoprovides high data rates and low latency. The RAN's controller controlsthe nodes that are connected to it. The network controller performsradio resource management, mobility management, and data encryption. Itconnects to the circuit-switched core network and the packet-switchedcore network, depending on the type of RAN. The RAN architecturesseparate the user plane from the control plane into different networkelements. In this scenario, the RAN controller can exchange user datamessages through one software-defined networking (SDN) switch, and asecond set with base stations via a second control-based interface. Thisseparation of the control plane and data plane will be an essentialaspect of the flexible 5G radio access network, as it aligns with SDNand network functions virtualization (NFV) techniques such as servicechaining and network slicing.

In one implementation of one or more gNBs and one or more UEs in whichsystems and methods for supporting ultra-reliable low-latencycommunication (URLLC) service and associated numerologies in fifthgeneration (5G) New Radio (NR) may be implemented. The one or more UEscommunicate with one or more gNBs using one or more physical antennas.For example, a UE transmits electromagnetic signals to the gNB andreceives electromagnetic signals from the gNB using the one or morephysical antennas. The gNB communicates with the UE using one or morephysical antennas.

The UE and the gNB may use one or more channels and/or one or moresignals to communicate with each other. For example, the UE may transmitinformation or data to the gNB using one or more uplink channels.Examples of uplink channels include a physical shared channel (e.g.,PUSCH (Physical Uplink Shared Channel)), and/or a physical controlchannel (e.g., PUCCH (Physical Uplink Control Channel)), etc. The one ormore gNBs may also transmit information or data to the one or more UEsusing one or more downlink channels, for instance. Examples of downlinkchannels physical shared channel (e.g., PDCCH (Physical Downlink SharedChannel), and/or a physical control channel (PDCCH (Physical DownlinkControl Channel)), etc. Other kinds of channels and/or signals may beused.

Each of the one or more UEs may include one or more transceivers, one ormore demodulators, one or more decoders, one or more encoders, one ormore modulators, a data buffer and a UE operations module. For example,one or more reception and/or transmission paths may be implemented inthe UE. The transceiver may include one or more receivers and one ormore transmitters. The one or more receivers may receive signals fromthe gNB using one or more antennas. For example, the receiver 120 mayreceive and downconvert signals to produce one or more received signals.The one or more received signals may be provided to a demodulator. Theone or more transmitters may transmit signals to the gNB using one ormore physical antennas. For example, the one or more transmitters mayupconvert and transmit one or more modulated signals.

The demodulator may demodulate the one or more received signals toproduce one or more demodulated signals. The one or more demodulatedsignals may be provided to the decoder. The UE may use the decoder todecode signals. The decoder may produce decoded signals, which mayinclude a UE-decoded signal (also referred to as a first UE-decodedsignal). For example, the first UE-decoded signal may comprise receivedpayload data, which may be stored in a data buffer. Another signalincluded in the decoded signals (also referred to as a second UE-decodedsignal) may comprise overhead data and/or control data. For example, thesecond UE-decoded signal may provide data that may be used by the UEoperations module to perform one or more operations. In general, the UEoperations module may enable the UE to communicate with the one or moregNBs. The UE operations module may include one or more of a UE URLLCmodule. With regard to NR, some considerations with SR include trafficcharacteristics, logical channel/logical channel group, the amount ofdata available, information related to numerology and/or TransmissionTime Interval (TTI) duration, and the priority of data.

Short latency in NR may be important to support services like URLLC.This may impact the design of the SR. The design of the SR in amulti-numerology/TTI duration configuration also influences the latency.With regard to NR, some considerations for SR latency and periodicityinclude: major design changes related to SR latency and periodicitycompared to LTE; what is the impact from the NR latency requirements;what is the impact from a multiple numerology/TTI durationconfiguration; and what is the impact from other functions designed toreduce latency (e.g., grant-free transmissions and Semi-PersistentScheduling (SPS)).

The function of the Buffer Status Report (BSR) in LTE is for the UE toreport the amount of available data in the UE to the eNB. The eNB canthen use this information to set the size of the UL grant. Logicalchannels are grouped together in logical channel groups (LCGs). A BSR istriggered if data becomes available in an LCG and all other LCGs have nodata, or if data belonging to a logical channel with a higher prioritythan all other LCGs becomes available, or if there is room in the MACProtocol Data Unit (PDU) to send a BSR instead of padding. There may betwo timers which upon expiry trigger BSR. A BSR contains information onthe amount of data available per logical channel group. The BSR iscarried as a MAC control element (CE) in a MAC PDU. Like the SR, thedesign of the BSR for NR may be impacted by the multi-numerology/TTIduration configuration supported in NR. The systems and methodsdescribed herein provide mechanisms for BSR for NR. Uplink scheduling isa key functionality to meet a broad range of use cases includingenhanced mobile broadband, massive MTC, critical MTC, and additionalrequirements. Buffer Status Reports (BSRs) on the other hand carry moredetailed information compared to SR. A BSR indicates buffer size foreach LCG. However, the BSR requires a grant for transmission so it maytake a longer time until the gNB receives it since it may need to bepreceded by an SR. The framework with SR/BSR from LTE may be improved.In an approach, the SR/BSR scheme from LTE can be reused in NR as abaseline. NR should support a wide spread of use cases which havedifferent requirements. In some use cases (e.g., critical MTC andURLLC), NR has tighter latency requirements than has been considered forLTE so far. Also, services such as eMBB can enjoy the enhancements to SRand BSR. In NR, modifications of SR/BSR aim to report the UE bufferstatus (e.g., priority and the buffer size) as well as wantednumerology/TTI duration within the given time constraints. It is assumedthat a mapping of logical channel (LCH) to LCG to numerology/TTIduration will make it possible to infer which numerology/TTI duration touse given the LCG. Hence no explicit signaling of numerology/TTIduration is needed in the SR/BSR if an LCG (or LCH) is present in theSR/BSR. Considering the limitations identified above, it is possible toeither enhance SR with more information bits to indicate moreinformation or enhance BSR.

URLLC provides 1 ms end-to-end radio link latency and guaranteed minimumreliability of 99.999%, which are crucial for some URLLC use cases. SomeURLLC uses cases are described herein and how they map to requirementsat a high level. A URLLC terminal (e.g., UE) will get a benefit frompacket duplication. Radio Link Control (RLC) retransmission (ARQ) is notassumed to be used for meeting the strict user plane latencyrequirements of URLLC. A URLLC device MAC entity may be supported bymore than one numerology/TTI durations. The NR design aims to meet theURLLC QoS requirements only after the control plane signaling forsession setup has completed (to eliminate the case that the UE isinitially in idle). Discontinuous reception (DRX) design will notoptimize for URLLC service requirements. For DL, dynamic resourcesharing between URLLC and eMBB is supported by transmitting URLLCscheduled traffic. URLLC transmission may occur in resources scheduledfor ongoing eMBB traffic. Asynchronous and adaptive HARQ is supportedfor URLLC DL. At least an UL transmission scheme without grant issupported for URLLC. Resources may or may not be shared among one ormore users.

In an implementation, mini-slots have the following lengths. At leastabove 6 GHz, mini-slot with length 1 symbol supported. Lengths from 2 toslot length −1 may be supported. It should be noted that some UEs 102targeting certain use cases may not support all mini-slot lengths andall starting positions. Mini-slots can start at any OFDM symbol, atleast above 6 GHz. A mini-slot may contain Demodulation RS(s) (DM-RS) atposition(s) relative to the start of the mini-slot.

A wide range of URLLC use cases may be supported by NR. 5G aims tosupport a broad range of use cases (or services) and enableground-breaking performance of the URLLC devices (e.g., robots, smartcars, etc.). Some URLLC applications are discussed herein.

One URLLC use case is robotics. 5G needs to improve the response timefor diagnostic situations. For instance, in the near future, robots willbe very low-cost, since robots will only carry around a set of sensors,cameras, actuators and mobility control units. All the intelligentcomputation system, requiring expensive hardware, may be remotely run onan edge cloud.

The sensors and cameras on the robots may be used to monitor theenvironment and capture the data in real time. The captured data will beimmediately transmitted to a central system in a few milliseconds. Thecenter processes the data in an intelligent way (e.g., based on machinelearning and AI (artificial intelligent) algorithms) and makes decisionsfor the robots. The decision/commands may be delivered to the robot veryquickly and the robots will follow the instructions.

The targeted maximum round trip time for this kind of robotic scenariois Ims. This may include starting with capturing data, transmitting thedata to the center, progressing data on the center and sending thecommand to the robot, and running the received command.

Another URLLC use case is industrial automation. Industrial automation(together with MTC) is one of the key applications that are consideredwithin 5G systems. Current industrial control systems rely on fast andreliable wired links. However, there exists a large interest inutilizing flexible wireless systems provided by 5G in the future.

This use case considers a combined indoor factory environment, where anumber of objects (e.g., robots, self-driving heavy machines, etc.)perform various dedicated tasks as parts of a production process. Allthese objects are controlled by a production center. These kinds ofindustrial applications require a guaranteed reliability, higher datarate and minimum end-to-end latency within various control processes.

Another URLLC use case is remote surgery and health care. Remote surgerycan be considered as another 5G URLLC use case. With a sense of touch,5G can enable a surgeon to diagnose (e.g., identify cancerous tissue)where the specialist and the patient physically are not able to bepresent in the same room/environment.

In this 5G medical use case, there may be a robotic end which in realtime will provide the sense of touch to the surgeon during a minimallyinvasive surgery. The sense of touch will be captured at the robotic endand, with a latency of few milliseconds, the sensed data will bereflected to the surgeon who is at the other end and wears hapticgloves. On top of that, the surgeon needs to be able to remotely controlthe robotic end as well in a visualized environment. In the remotesurgery scenario, the e2e latency is ideally in the order of severalmilliseconds.

Another URLLC use case is interactive augmented-virtual reality. Ahigh-resolution augmented-virtual reality system is an efficient way todisplay a real or manipulated environment in three-dimensions foreducational purposes, for instance. In one scenario, a number oftrainees are connected in a virtualized real environment/systemsimulator, where the trainees are able to jointly/collaborativelyinteract with each other by perceiving the same environment and the sameartificial subjects and objects. Since the scenario requires interactionbetween the trainees in real time, the targeted round-trip time fromtrainee to the simulator and from simulator back to the trainee shouldbe in the order of milliseconds and not exceed human perception time.

Another URLLC use case is smart vehicles, transport and infrastructure.Self-Driving vehicles can be interpreted as automated driving wherevehicle-to-infrastructure (e.g., smart bus stop, smart traffic lights,etc.) and vehicle-to-vehicle real-time communication is required. Allthese communications can be coordinated in real time by a centralizedsystem (e.g., Intelligent Traffic Management Center (ITMC)).

In such a scenario, the ITMC aims to estimate hazardous conditions wellin advance and decrease the risk of traffic accidents. As an example, asan intelligent system, the ITMC can monitor attributes of the objects inthe traffic based on the object's received data. By doing that, fatalsituations will be anticipated and the system will interact directly(e.g., steer vehicles) even before the drivers to prevent accidents. Inthis kind of traffic scenario, round-trip latencies from vehicles toITMC and ITMC to the vehicles in the order of milliseconds will increasethe traffic safety.

Another URLLC use case is drones and aircraft communication. Drones aregetting increasingly important, especially in the surveillance, publicsafety and media domain. All of these domains come under the criticalcommunication with strict requirements on latency and reliability. Themotivation for such requirements varies from mission criticality tomonetary benefits (e.g., coverage of sports events using drones leadingto in-demand content with high copyrights cost).

Latency and reliability are key factors to control the drones given thenature of use cases considered. Similarly, aircraft communication isalso being considered using NR which also demands the highest standardof reliability and strict latency requirements. The long distances andmobility aspects together with latency and reliability requirementspresent challenges in this use case.

As observed by these use cases, in some URLLC scenarios, mobility is akey requirement together with latency and reliability. A core need ofeach URLLC use case is reliability and latency and these needs shouldhave precedence over resource efficiency due to criticality of thescenarios.

Both International Telecommunication Union (ITU) and 3GPP have defined aset of requirements for 5G, including URLLC. For URLLC reliability, therequirement is the same, whereas for URLLC latency, 3GPP places astricter requirement of 0.5 ms one-way end-to-end latency in UL and DL,compared to Ims in ITU.

3GPP has agreed on the following relevant requirements. Reliability canbe evaluated by the success probability of transmitting X bytes within acertain delay, which is the time it takes to deliver a small data packetfrom the radio protocol layer 2/3 SDU ingress point to the radioprotocol layer 2/3 SDU egress point of the radio interface, at a certainchannel quality (e.g., coverage-edge). A general URLLC reliabilityrequirement for one transmission of a packet is 1-105 for 32 bytes witha user plane latency of Ims.

User plane (UP) latency can be described as the time it takes tosuccessfully deliver an application layer packet/message from the radioprotocol layer 2/3 SDU ingress point to the radio protocol layer 2/3 SDUegress point via the radio interface in both uplink and downlinkdirections, where neither device nor base station reception isrestricted by DRX. For URLLC, the target for user plane latency shouldbe 0.5 ms for UL, and 0.5 ms for DL. Furthermore, if possible, thelatency should also be low enough to support the use of the nextgeneration access technologies as a wireless transport technology thatcan be used within the next generation access architecture. The valueabove should be considered an average value and does not have anassociated high reliability requirement.

According to IMT 2020, LTE Rel-15 should be able to separately fulfilllow latency and reliability requirements. Low latency may be defined asthe one-way time it takes to successfully deliver an application layerpacket/message from the radio protocol layer 2/3 SDU ingress point tothe radio protocol layer 2/3 SDU egress point of the radio interface ineither uplink or downlink in the network for a given service in unloadedconditions, assuming the mobile station is in the active state. In IMT2020, the minimum requirements for user plane latency is 1 ms for URLLC.

Reliability may be defined as the success probability of transmitting alayer 2/3 packet within a required maximum time, which is the time ittakes to deliver a small data packet from the radio protocol layer 2/3SDU ingress point to the radio protocol layer 2/3 SDU egress point ofthe radio interface at a certain channel quality (e.g., coverage-edge).This requirement is defined for the purpose of evaluation in the relatedURLLC test environment.

The minimum requirement for the reliability is 1-10-5 successprobability of transmitting a data packet of size (e.g., 20 bytes) byteswithin 1 ms in channel quality of coverage edge for the Urbanmacro-URLLC test environment.

Apart from the ITU and 3GPP requirements, there are other interestingcombinations of latency and reliability that may apply to future usecases. One such case is a wide-area scenario with a more relaxed latencybut with high reliability. Therefore, we argue that a network should beable to configure a wide range of latency-reliability settings. Toenable this, several different technological components may beconsidered for URLLC. Therefore, URLLC may fulfil IMT 2020 requirementsand also a wider range of requirements relevant for future use cases.

As mentioned above, a wide range of performance requirements calls for aset of tools for the network to apply according to use case andscenario. At the physical layer, this can include enhanced coding,diversity, repetitions, and extra robust control and feedback. At higherlayers, the focus is fast and reliable scheduling, data duplication, andmobility robustness.

Diversity is a key to achieve high reliability. Whereas one singletransmission (including control message) can be robust (e.g., low BLER),it requires a very low code rate and therefore wide allocations to reachthe target. With diversity, the transmission is spread out in time,space, and frequency, exploiting variations in the channel to maximizethe signal.

In time domain, at least two main options may be employed. One option isthat the transmission is extended over more OFDM symbols and thereby thecode rate is reduced. Alternatively, the transmission is repeated. Arepetition can be automatic (bundled transmissions), or a retransmissiontriggered by feedback.

In frequency domain, the transmission of control and data may berepeated on multiple carriers to exploit frequency diversity of thechannel. Frequency repetition of data can be done on lower layers (e.g.,MAC) or in higher layers (e.g., PDCP). Another possibility for achievingfrequency diversity is to spread out parts of the transmissions over awider bandwidth.

For UL transmissions, the basic access may be based on a schedulingrequest (SR). The SR may be followed by an UL grant, and only afterreceiving this grant can the UE transmit UL data. The two firsttransmissions (SR and grant) cause an extra delay, which may be an issuefor delay sensitive traffic. Latency reduction is a feature in LTE-14 toscale down the minimum schedulable time unit so that the absolute timeduration of the first two transmissions is scaled down proportionally.Similar principles can be applied to 5G with tools such as highernumerology. This, in principle, can satisfy the latency requirements andallow several HARQ retransmissions round-trip-time that further enhancethe reliability. However, with higher numerology, it poses challenges tosupport wide-area deployment with power-limited UEs 102 and requires alarger bandwidth. Last but not the least, additional works to enhancereliability for SR and UL grant are required.

As an alternative, the UL grant can be configured (e.g., like SPS UL)with skip padding in LTE. This may be referred to as “Fast UL.” WithFast UL, the UE has a configured UL grant that it may use when it has ULdata. In this setup, the UL latency is similar to that of DL, making itan important enhancement for URLLC.

Given the large BW allocations expected for URLLC UL traffic, aconfigured grant where the gNB 160 pre-allocates a part of the band to aUE can lead to UL capacity problems. This leads to even larger resourcewaste if the URLLC UL traffic is less frequent and sporadic. This issuecan be solved if the same time-frequency resource can be given tomultiple UEs 102.

Collisions may occur in contention-based access. To satisfy the strictURLLC requirements, resolutions must be resolved in a reliable way andremedial solutions may be in place in the event of the collisions. As abaseline, reliable UE identification should be available forcontention-based access in the case of collided transmissions. Afterdetecting the collision, fast switching to grant-based resources shouldbe available. In addition, automatic repetitions with a pre-definedhopping pattern can reduce requirements on collision probability and UEidentification detection.

The requirement on latency and reliability is not only for static UEs102, but also for UEs 102 with different mobility levels for differentuse cases.

Increased robustness can be achieved at higher layers by transmittingduplicates of the data in either the spatial domain (e.g., DualConnectivity), frequency domain (e.g., Carrier Aggregation), or in timedomain with MAC/RLC layer duplication. Optionally, without duplication,better reception quality can be achieved by properly selecting between aset of available connecting links (e.g., Multiple Connectivity).

In another aspect, a buffer status reporting (BSR) procedure may be usedto provide the serving eNB 160 with information about the amount of dataavailable for transmission in the UL buffers associated with the MACentity. RRC controls BSR reporting by configuring the three timersperiodic BSR-Timer, retxBSR-Timer and logicalChannelSR-ProhibitTimer andby, for each logical channel, optionally signaling logicalChannelGroup,which allocates the logical channel to a Logical Channel Group (LCG).

For the Buffer Status reporting procedure, the MAC entity may considerradio bearers that are not suspended and may consider radio bearers thatare suspended. For narrowband Internet of Things (NB-IoT), the Long BSRis not supported and all logical channels belong to one LCG.

A (BSR) may be triggered if any of the following events occur. A BSR maybe triggered if UL data, for a logical channel which belongs to a LCG,becomes available for transmission in the RLC entity or in the PDCPentity and either the data belongs to a logical channel with higherpriority than the priorities of the logical channels which belong to anyLCG and for which data is already available for transmission, or thereis no data available for transmission for any of the logical channelswhich belong to a LCG. In this case, the BSR may be referred to as a“Regular BSR.”

A BSR may also be triggered if UL resources are allocated and the numberof padding bits is equal to or larger than the size of the BSR MACcontrol element plus its subheader. In this case, the BSR may bereferred to as a “Padding BSR.”

A BSR may also be triggered if the retxBSR-Timer expires and the MACentity has data available for transmission for any of the logicalchannels which belong to a LCG. In this case, the BSR may be referred toas a “Regular BSR.”

A BSR may also be triggered if a periodicBSR-Timer expires. In thiscase, the BSR may be referred to as a “Periodic BSR.”

For a Regular BSR, if the BSR is triggered due to data becomingavailable for transmission for a logical channel for whichlogicalChannelSR-ProhibitTimer is configured by upper layers, a UE maystart or restart the logicalChannelSR-ProhibitTimer. Otherwise, ifrunning, the UE may stop the logicalChannelSR-ProhibitTimer.

For Regular and Periodic BSR, if more than one LCG has data availablefor transmission in the TTI where the BSR is transmitted, the UE mayreport a Long BSR. Otherwise, the UE may report a Short BSR.

For a Padding BSR, if the number of padding bits is equal to or largerthan the size of the Short BSR plus its subheader but smaller than thesize of the Long BSR plus its subheader and if more than one LCG hasdata available for transmission in the TTI where the BSR is transmitted,the UE may report a truncated BSR of the LCG with the highest prioritylogical channel with data available for transmission. Otherwise, the UEmay report a Short BSR. If the number of padding bits is equal to orlarger than the size of the Long BSR plus its subheader, the UE mayreport a long BSR.

If the BSR procedure determines that at least one BSR has been triggeredand not cancelled and if the MAC entity has UL resources allocated fornew transmission for this TTI, then the UE may instruct the Multiplexingand Assembly procedure to generate the BSR MAC control element(s). TheUE may start or restart the periodicBSR-Timer except when all thegenerated BSRs are Truncated BSRs. The UE may start or restart aretxBSR-Timer.

If a Regular BSR has been triggered and logicalChannelSR-ProhibitTimeris not running, and if an uplink grant is not configured or the RegularBSR was not triggered due to data becoming available for transmissionfor a logical channel for which logical channel SR masking(logicalChannelSR-Mask) is setup by upper layers, then a SchedulingRequest may be triggered.

A MAC PDU may contain at most one MAC BSR control element, even whenmultiple events trigger a BSR by the time a BSR can be transmitted inwhich case the Regular BSR and the Periodic BSR have precedence over thepadding BSR. The MAC entity shall restart retxBSR-Timer upon indicationof a grant for transmission of new data on any UL-SCH.

All triggered BSRs may be cancelled in case the UL grant(s) in this TTIcan accommodate all pending data available for transmission but is notsufficient to additionally accommodate the BSR MAC control element plusits subheader. All triggered BSRs may be cancelled when a BSR isincluded in a MAC PDU for transmission.

The MAC entity may transmit at most one Regular/Periodic BSR in a TTI.If the MAC entity is requested to transmit multiple MAC PDUs in a TTI,it may include a padding BSR in any of the MAC PDUs which do not containa Regular/Periodic BSR.

All BSRs transmitted in a TTI may reflect the buffer status after allMAC PDUs have been built for this TTI. Each LCG may report at the mostone buffer status value per TTI and this value may be reported in allBSRs reporting buffer status for this LCG.

It should be noted that padding BSR is not allowed to cancel a triggeredRegular/Periodic BSR, except for NB-IoT. A Padding BSR is triggered fora specific MAC PDU only and the trigger may be cancelled when this MACPDU has been built.

A MAC PDU is a bit string that is byte aligned (i.e., multiple of 8bits) in length. As described herein, bit strings are represented bytables in which the most significant bit is the leftmost bit of thefirst line of the table, the least significant bit is the rightmost biton the last line of the table, and more generally the bit string is tobe read from left to right and then in the reading order of the lines.The bit order of each parameter field within a MAC PDU is representedwith the first and most significant bit in the leftmost bit and the lastand least significant bit in the rightmost bit.

MAC SDUs are bit strings that are byte-aligned (i.e., multiple of 8bits) in length. An SDU is included into a MAC PDU from the first bitonward. The MAC entity may ignore the value of Reserved bits in downlinkMAC PDUs.

A MAC PDU includes a MAC header, zero or more MAC Service Data Units(MAC SDU), zero, or more MAC control elements, and optionally padding,as illustrated in FIG. 4. Both the MAC header and the MAC SDUs may be ofvariable sizes. A MAC PDU header may include one or more MAC PDUsubheaders. Each subheader may correspond to either a MAC SDU, a MACcontrol element or padding. Examples of MAC PDU subheaders are describedin connection with FIG. 5.

A MAC PDU subheader may include the five or six header fieldsR/F2/E/LCID/(F)/L but for the last subheader in the MAC PDU and forfixed sized MAC control elements. The last subheader in the MAC PDU andsubheaders for fixed sized MAC control elements may include the fourheader fields R/F2/E/LCID. A MAC PDU subheader corresponding to paddingincludes the four header fields R/F2/E/LCID.

MAC PDU subheaders may have the same order as the corresponding MACSDUs, MAC control elements and padding. MAC control elements may beplaced before any MAC SDU. Padding may occur at the end of the MAC PDU,except when single-byte or two-byte padding is required. Padding mayhave any value and the MAC entity may ignore it. When padding isperformed at the end of the MAC PDU, zero or more padding bytes areallowed. When single-byte or two-byte padding is required, one or twoMAC PDU subheaders corresponding to padding are placed at the beginningof the MAC PDU before any other MAC PDU subheader. A maximum of one MACPDU can be transmitted per Transport Block (TB) per MAC entity. Amaximum of one MCH MAC PDU can be transmitted per TTI.

In the system of FIG. 1A-1D, multiple-input, multiple-output antennasystems coordinate two or four antennas at a time to simultaneously senddata over the same radio channel, increasing data speeds. A phone mighthave a 4×2 MIMO system with 4 receiving (downloading) antennas and 2transmitting (uploading) antennas, with up to an 8×8 array for 5G. Toaddress multiple customers at once, new cell towers will include“massive” 128-antenna arrays with 64 receiving and 64 transmittingantennas. In one embodiment, each antenna of the phone and the celltower is individually steerable. The steering can be done usingindividual motor/actuator, or can be done as a small group of 2×2antennas on the cell tower that communicate with a particular phone. Agroup of antennas can be coordinated to beam at each other. This can bedone using neural network or machine learning to provide real time beamsteering. Moreover, the antennas support carrier aggregation thatenables a radio to increase data capacity. Known as “channel bonding,”5G supports aggregation of up to 16 channels at once, including mixes ofseparate 4G and 5G frequencies.

FIG. 1E shows an exemplary 5G millimeter wave frame structure. As shownDL refers to downlink transmission from eNB to UEs and UL refers touplink transmission from UEs to eNB. As shown control and data planesare separate, which helps in achieving lesser latency requirements. Thisis due to the fact that processing of control and data parts can run inparallel. The mm wave has small antenna and hence large number ofantennas are packed in small size. This leads to use of massive MIMO ineNB/AP to enhance the capacity. Dynamic beamforming is employed andhence it mitigates higher path loss at mm wave frequencies. 5Gmillimeter wave networks support multi-gigabit backhaul upto 400 metersand cellular access upto 200-300 meters. Hover, 5G millimeter wave goesthrough different losses such as penetration, rain attenuation etc. Thislimits distance coverage requirement of mm wave in 5G based cellularmobile deployment. Moreover path loss at mm is proportional to square ofthe frequency. It supports 2 meters in indoors and about 200-300 metersin outdoors based on channel conditions and AP/eNB height above theground. It supports only LOS (Line of Sight) propagation and foliageloss is significant at such mm wave frequencies. Power consumption ishigher at millimeter wave due to more number of RF modules due to morenumber of antennas. To avoid this drawback, hybrid architecture whichhas fewer RF chains than number of antennas need to be used at thereceiver. Moreover low power analog processing circuits are designed inmm wave hardware.

Between bands 30 Ghz and 300 Ghz, mmWave promises high-bandwidthpoint-to-point communications at speeds up to 10 Gbps. But the signalsare easily blocked by rain or absorbed by oxygen, which is one reasonwhy it only works at short ranges. Beamforming is a way to harness themmWave spectrum by directly targeting a beam at a device that is in lineof sight of a base-station. But that means antennas in devices, andbase-stations on network infrastructure, have to be designed to handlethe complexity of aiming a beam at a target in a crowded cellularenvironment with plenty of obstructions. 5G femto cells can be used toextend 5G coverage inside buildings, for example. In 3GPP terminology, aHome NodeB (HNB) is a 3G femtocell. A Home eNodeB (HeNB) is an LTE 4Gfemtocell. The range of a standard base station may be up to 35kilometres (22 mi), and in practice could be 5-10 km (3-6 mi), amicrocell is less than two kilometers wide, a picocell is 200 meters orless, and a femtocell is in the order of 10 meters.

FIGS. 1E and 1F depict a cell phone that has an RF part including RFTransceiver chip, baseband part comprising of DSP and CPU forcontrolling the data/control messages. ADC/DAC chips are used forinterfacing both RF and baseband parts. The other basic cell phonecomponents include touchscreen display, battery, RAM, ROM, RF antenna,MIC, Speaker, camera, diplexer, micro-USB, SIM slots and others. FIG. 1Fshows an exemplary 5G cell phone architecture. As shown the architectureinclude baseband part, digital RF interface such as DigRF, ADC/DAC andRF Transceiver. The basic components are same in the 5G phone exceptantenna array is used instead of one antenna to support massive MIMO andbeamforming. Quadplexer is used instead of diplexer to support multiplebands. Quadplexer or Quadruplexer is used to multiplex and demultiplexfour radio frequencies to/from single coaxial cable as shown. This helpsin reducing cost and weight as well as uses very smaller area of thephone. This shown 5G cell phone architecture supports millimeter wavefrequency bands. In order to support massive MIMO/beamforming multiplePAs, LNAs, phase shifters, RF filters and SPDT switches are incorporatedin the 5G cell phone design. The 5G phone is backward compatible to2G/3G/4G, WLAN, Bluetooth, GNSS etc. The 5G phone shown is based onheterodyne architecture and advantages of Heterodye receiver. RadioFrequency Front End (RFFE) control signals are used to carry transmittersignal strength indicator (TSSI) and receiver signal strength indicator(RSSI) informations. The temperature control of the beamforming moduleand its calibration are performed. PMUs (Power Management Units) andLDOs (low drop-out regulators) are used in beamforming part of the 5Gcell phone. They transform DC voltage of coaxial cable to differentpower supplies for use in various dies for cell phone operation.

The RF frontend transceiver can realize the beam scanning functionthrough a plurality of antenna elements, T/R switches, power amplifierin the transmitter, low noise amplifiers in the receiver, low noiseswitches, phase shifters, and RF signals. The transceiver switches andthe low loss switches can control whether the antenna elements in thesystem receive RF signals or transmit RF signals. When the RF signalsare controlled to be transmitted, the RF signals have different phaseinformation for each link through the phase shifters, and then the RFsignals are amplified by the power amplifiers, which consists of apre-power amplifier and a power amplifier, and finally RF signals aretransmitted to the antenna elements. With different phases of theantenna elements, antenna array can form different beam directions, sothat an optimum beam pointing can be achieved in real time.

Since numerous antennas need to be provided on the mobile device, anantenna system applied in the metal back cover of the 5G mobileterminal, which includes a metal back cover, a signal feeder line, and aplurality of antenna elements. Preferably 3D printing to create acapacitive coupled patch antenna array capable of providing high gainand 360-degree coverage in the elevation plane. A material with arelative dielectric constant 2.2 and loss tangent 0.0009 at thefrequency band of 24-28 GHz is used as the substrate for printed circuitboard (PCB). The patches are printed at the top layer of the substrate.The bottom layer of the substrate consists of the ground plane. Theinner conductor of the coaxial probe feed extends from the ground planethrough the PCB substrate to reach the top layer feed which capacitivelycouples the patches. The antenna element covers 24-28 GHz, which is apossible frequency band for future 5G applications. Four sub-arrays of12 antenna elements, each providing 90 degrees in the elevation plane,were integrated into the mobile phone chassis for 360-degree coverage.The antenna array achieved a high gain of 16.5 dBi in the boresight andcan be steered from −60° to 60° in the phi plane. The physical size ofthe antenna is relatively small compared to existing designs, meaningthat it consumes less space and more antenna elements can be arrangedalong the width of the mobile phone ground plane. The bandwidth of theantenna is sufficient for 5G applications and can be further widened bymodifying the antenna structure.

In one embodiment, the case of the mobile device can have a plurality ofchannels where a liquid metal can be pumped into the right location andbe used as antennas. Such antenna scan be a liquid metal whose shape canchange according to the frequency. A plurality of microchannels areformed as part of the case, and the liquid metal can move as needed toaim at the antenna on the cell tower, and also to be away from theuser's face to minimize radiation on the cells and to reduce RFblockage. The liquid can be a eutectic alloy of Ga and In, which remainsin liquid form at room temperature, into very small channels the widthof a human hair. The channels are hollow with openings at either end butcan be any shape. Once the alloy has filled the channel, the surface ofthe alloy oxidizes, creating a “skin” that holds the alloy in placewhile allowing it to retain its liquid properties. The alloy can beinjected into elastic silicone channels, creating wirelike antennas thatare resilient and that can be manipulated into a variety of shapes.Since the frequency is determined by the antenna's size/shape, it can betuned by stretching it. Flexibility and durability are also idealcharacteristics, since the antenna could be folded or rolled up into asmall package for deployment and then unfolded again without any impacton its function. Salt water or other liquid metals or alloys couldreduce the cost.

In another embodiment, a 3D PCB utilizes the thickness of the entiremobile phone, which is typically 7-9 mm, and can provide mechanicalsupport to the entire phone like a casing. By using a 3D rather than aflat shape, more space is created for placing PCB components in themobile phone, particularly for the additional 5G antenna elements alongwith the corresponding RFIC. The 5G antenna can be the liquid metaldiscussed above. In one implementation that uses PCB lines, foursub-arrays with three sub-arrays of proposed antenna elements ondifferent sides of the bottom edge region in PCB and one sub-array atthe sides of the top edge region in PCB, each sub-array has 24 antennaelements and 96 antenna elements are used altogether. Each sub-arrayprovides 90° coverage in the theta plane.

In another embodiment, each antenna element has a feed probe, aninsulating sleeve, and a reflecting cavity. The reflecting cavity isformed by an inner concave of the outer side of a metal frame of themetal back cover. The reflecting cavity includes a first wall and asecond wall. One end of a feed probe is connected with the first walland a middle of the feed probe is connected with the second wall throughan insulating sleeve. The other end of the feed probe is connected witha signal feeder line. The 5G antenna can be located at a side of themobile terminal, which does not occupy the position of the traditionalantennas, so it can coexist with the 2G/3G/4G/GPS/WIFI/BT antennas. Thereflecting cavity can change a radiation direction of the 5G antenna toreduce the electromagnetic radiation on the user. In one embodiment, ifthe user puts the phone next to the face, sensors detect such usagescenario, and uses the 5G antennas most away from the user to increaseantenna efficiency and reduce radiation on the user.

Further, the shape of the reflecting cavity is a cuboid, and theantenna's operating wavelength is λ, and the length, width, and heightof the reflecting cavity are ranging from to λ, from 1/10λ to ½λ, andfrom ⅛λ to ½λ, respectively. The 5G antenna with the above reflectingcavity can produce a better directional radiation. Further, the metalback cover comprises a bottom case and a frame, and the first wall canbe a part of the metal bottom case a part of the metal frame. When thefirst wall is a part of the bottom case, the opening of the reflectingcavity is disposed on the frame. When the first wall is a part of theframe, the of the reflecting cavity is disposed on the bottom case.Further, the reflecting cavity can be filled with low loss materialswhose permittivity is larger than 1 and whose dielectric loss is lessthan 0.02, for example, plastic. The reflecting cavity can be filledwith different materials or filled partially through injection molding.The corresponding filling methods and materials can be selectedaccording to a beam scanning range of the antenna. When the reflectingcavity is filled with plastic material, the distance between elementscan be reduced therefore the scanning angle can be increased, but thebandwidth of the antenna will be reduced. The coupling between elementswill be increased and the radiation efficiency of the antenna will bedecreased. If it is necessary, the reflecting cavity can be filled withair. Further, a feed hole is set in the first wall, and the feed probeis connected with the feed hole. The end of the feed probe connectedwith the feed hole has a larger diameter. The feed probe has a screwstructure. The longitudinal section of the feed probe can be a T shapeor a triangular or a trapezoidal. The feed probe can be selectedaccording to the required bandwidth of the antenna element. The feedprobe with a T shape longitudinal has a narrow impedance bandwidth. Thefeed probe of the other forms have a wider impedance bandwidth, but itcan increase the length of the antenna element and reduce the scanningrange of the beam. Further, the antenna element is disposed on a longside of the metal back cover. 5G antenna is disposed on the side of themobile terminal through an antenna element constituted by a feed probeand a reflecting cavity. The antenna element is disposed on the side ofthe metal back cover. It is advantageous to form an array, thus it canachieve a high a wide beam width and beam scanning angle. Further, theantenna array includes N elements, and N is a positive integer which islarger than 1. The antenna array can achieve a high gain, a wide beamwidth and beam scanning angle. Further, the antenna array system appliedin the metal back cover includes at least two sub-arrays which aredisposed respectively at both long sides of the metal back cover. Theantenna array does not occupy the position of the traditional antennas,so it can coexist with 2G/3G/4G/GPS/WIFI/BT antennas, and it has a widebandwidth and a high gain, and can achieve a wide beam scanning angleand beam width in cooperation with antennas on 5G tower antennas.

Turning now to 5G cell towers, a 5G tower is different than a 4G towerboth physically and functionally: more are needed to cover the sameamount of space, they're smaller, and they transmit data on an entirelydifferent part of the radio spectrum. Small cells support high frequencymillimeter waves, which have limited range. The antennas within thesmall cell are highly directional and use what's called beamforming todirect attention to very specific areas around the tower. These devicescan also quickly adjust power usage based on the current load. The smallcell antenna needs to be installed with minimal disruption to localpeople—no street works or construction—and without changing the look ofthe area. They are connected using optical fiber high speed convergednetwork, which also supports other mobile technologies, home broadband,Internet of Things (IoT) and business services. The housing of themobile equipment can be done within street furniture such as manholecovers, lamp-posts and phone boxes to increase the speed and extend thecoverage of a mobile signal along busy roads, town squares and inshopping and entertainment areas. For example, the manhole coverantennae can be installed with minimal disruption to local people—nostreet works or construction—and without changing the look of the area,as the kit is below ground. By connecting the street furniture to 5Gnetwork, the fiber-connected 5G-enabled small antennae are thefoundation on which connected smart cities will be built. 5Gconnectivity will allow connected traffic lights instantly to rerouteroad traffic around congestion, councils automatically to schedulerepairs for broken infrastructure like street lighting, and businessesto manage how much energy they use intelligently.

The 5G ecosystem is expected to support high-density networks by addingnew features to the radios and to the overall system layout. Thetraditional combination in 3G/4G networks of a remote radio headconnected to an external antenna will be extended by active antennasystems (AAS) or active phased-array antennas with massive antennaelements (massive APAA's), in which the electronics will be embedded inthe antenna system and operating over a wide frequency range (600 MHz to28 GHz and above) GHz. This primary system will be supported bycomplementary systems in dense areas with a high number of antennas tosupport multi-user MIMO (MU-MIMO). These antenna elements will featuretheir own control electronics, requiring new connectivity solutions.Frequencies above 6 GHz will be predominately supported by highlyintegrated systems. These radio frequency integrated circuits (RFIC) canfeature integrated antennas on the top surface of the chipset.

FIG. 2A shows an exemplary light post mounted 5G antenna system mountedon a plurality of light posts 11. The light post 11 can also be atraffic light or street sign or utility pole. Small cells areperiodically placed on the traffic light, street sign, or utility polein a neighborhood. A system 1 with a computing unit 10 in communicationwith 5G antenna and city monitoring units, each monitoring unit arrangedto monitor an operational status of at least one street lighting device11. Hence, a single monitoring unit may be configured to monitor one orseveral lighting devices 11 with respect to operational status. Themonitoring units may e.g. be mounted in (or at or in the vicinity of)the street devices 11. In the present example, the street devices 11 areroad lamps arranged to illuminate a road 15 but may alternatively be anyother kind of street devices, such as traffic enforcements cameras ortraffic lights. The computing unit 10 may be in communication with auser interface 19 and a database 18 (or memory or any other means) forstoring region description data. The region description data may e.g. bea region map (such as a road map or geographical map) and/or dataindicative of industrial areas, parks, museums parking lots, averagenumber of people in the region or any other information which may beutilized to prioritize regions e.g. with respect to maintenance urgency.The region description data may be presented e.g. in a map and/or atable over a region in which the street devices 11 are located.

The city/traffic light post cellular device can communicate with acellular device belonging to a person who is crossing a street near thecity light or street light. This is confirmed with camera detection ofthe person crossing the street and if confirmed, the cellular deviceemits a person to vehicle (P2V) or a vehicle to person (V2P) safetymessage to oncoming vehicles to avoid a collision. This system can helpelderly users cross the street safely. The quick speed of the 5G networkenables cars, bikes, and moving vehicles to stop quickly to protect theperson in an emergency where the person is crossing the street withoutadvanced notice to others.

In another embodiment, the camera can detect a pedestrian or personwalking and facing a crossing point. The system sends a confirmation tothe person's cell phone indicating whether the person desires to crossthe street. Once confirmed the system can look up oncoming traffic todetermine a gap in traffic to allow the user to cross the street.Alternatively, instead of automated traffic crossing detection using thecamera, a walking person activates a street button or a cell devicepointing to a desired traversal, the person waits for an indication tocross the street, the system can identify a gap in traffic and signalvehicles behind the gap to stop at the intersection and allow the userto traverse the desired path. After the person safely reaches the otherside of the street, the system can signal vehicles to move again. Thecameras can capture scenarios including: vehicle going straight, vehicleturning right, vehicle turning left, pedestrian crossing, pedestrian inthe road, and pedestrian walking adjacent to the road. The vehicle goingstraight and the pedestrian crossing scenario is the most frequentpre-crash scenario and has the highest cost. The vehicle turning (rightor left) scenarios result in less severe injuries, V2P systemsfunctioning correctly within these scenarios would help maximize crashavoidance. The vehicle going straight and pedestrian either in road oradjacent to the road is lower in occurrence but these crashes tend toresult in fatalities.

In addition to pedestrian assistance, the 5G vehicle communication andcamera combination can handle the following patterns as well:

-   -   Intersection Movement Assist (IMA) warns drivers when it's        unsafe to enter an intersection due to high collision        probability with other vehicles at intersections. The street        cameras capture location information from the “cross traffic”        vehicle enables the vehicle attempting to cross the intersection        to avoid danger, even if the view is blocked.    -   Electronic Emergency Brake Light (EEBL) enables a vehicle to        broadcast a self-generated emergency brake event to surrounding        vehicles. Upon receiving information from the cameras, the        processor determines the relevance of the event and, if        appropriate, provides a warning to the cars/drivers, helping to        prevent a crash.    -   Forward Collision Warning (FCW) warns drivers of an impending        rear-end collision with another vehicle ahead in traffic, in the        same lane and moving in the same direction. The camera, along        with data received from other vehicles, determines if a forward        collision is imminent and to warn drivers to avoid rear-end        vehicle collisions.    -   Blind Spot Warning (BSW) and Lange Change Warning (LCW) warn        drivers during a lane change attempt if the blind-spot zone into        which the vehicle intends to switch is, or will soon be,        occupied by another vehicle traveling in the same direction.        This is detected by the camera in conjunction with data from        vehicles, and the processor sends an advisory message to the        car/driver indicating a vehicle in the blind spot zone. When        attempting to merge into the same lane as the conflicting        vehicle, the processor sends a warning to the car/driver.    -   Do Not Pass Warning (DNPW) warns drivers during a passing        maneuver attempt when a slower-moving vehicle ahead cannot be        passed safely using a passing zone, because the passing zone is        occupied by vehicles moving in the opposite direction. A vehicle        sends out an indication on the V2V it will pass, and the camera        captures data and sends advisory information that the passing        zone is occupied when a vehicle is ahead and in the same lane,        even if a passing maneuver is not being attempted.    -   Left Turn Assist (LTA) warns drivers during a left turn attempt        when it is not safe to enter an intersection or continue in the        left turn attempt, due to a car approaching the same path with        no intent of stopping. The camera and processor can provide        collision warning information to the vehicle operational        systems, which may perform actions to reduce the likelihood of        crashes at intersections and left turns.

Each monitoring unit may be configured to continuously and/or atpredetermined time intervals and/or upon request (e.g. from thecomputing unit 10) measure (or check) the operational status of thestreet device 11. The operational status may e.g. be indicated byparameters such as light output, energy consumption or any otherparameter relating to the operational condition of the street device 11.Further, the operational status of the street device 11 may be indicatedby a failure signal. The monitoring units may be configured toautomatically transmit the failure indication signal in case the streetdevice is (or is soon) out of function. Further, the monitoring unitsmay be configured to store or measure the geographical positions of thestreet devices 11. For example, a monitoring unit (or the streetdevices) may comprise a GPS receiver for obtaining a GPS position of thestreet device 11.

The monitoring units may communicate (directly or indirectly) with thecomputing unit 10, preferably in an automatic manner. For example, themonitoring units may communicate with the computing unit 10 by means ofradio (or any wireless) communication and/or wired communication such aselectrical/optical communication (e.g. via Ethernet). The monitoringunits may communicate via other units (e.g. servers), which in turncommunicates with the computing unit. Hence, the computing unit 10 mayobtain information indicative of the operational statuses and positionsof the street devices 11 from a peripheral server, which has gatheredsuch information e.g. from the monitoring units.

FIG. 2B shows a block diagram of the unit 11. While the unit can includeconventional yellow sodium vapor lights, white light emitting diode(LED) light is preferred with an adaptive control system to provideenergy efficient lighting. Smart LED streetlights enable the city tomonitor energy consumption and provide the opportunity to dim lightinglevels during late evenings. The unit 11 includes an electronic nose todetect air pollution level. The electronic nose can simply be a MEMSdevice acting as a particle counter. Alternatively, the electronic nosecan detect composition of gas and provide a more detailed report, forexample identifying air pollution as gun power smell, illegal drugsubstance smell, car exhaust smell, industrial pollutant, or rottingmammal smell and such information can be relayed to suitable trashremoval contractors. The unit 11 also includes a microphone array thatcan detect sound and direction of sound. This is useful to detectinggunshots, and the direction of the sound can be triangulated to pinpointthe position of the shooting. The unit 11 also includes a camera, whichcan be a 360 degree camera. Alternatively, the camera can be a 3D camerasuch as the Kinect camera or the Intel RealSense camera for ease ofgenerating 3D models and for detecting distance of objects. To reduceimage processing load, each camera has a high performance GPU to performlocal processing, and the processed images, sound, and odor data areuploaded to a cloud storage for subsequent analysis. An embodiment ofthe electronic nose can be used that includes a fan module, a gasmolecule sensor module, a control unit and an output unit. The fanmodule is used to pump air actively to the gas molecule sensor module.The gas molecule sensor module detects the air pumped into by the fanmodule. The gas molecule sensor module at least includes a gas moleculesensor which is covered with a compound. The compound is used to combinepreset gas molecules. The control unit controls the fan module to suckair into the electronic nose device. Then the fan module transmits anair current to the gas molecule sensor module to generate a detecteddata. The output unit calculates the detected data to generate acalculation result and outputs an indicating signal to an operator orcompatible host computer according to the calculation result.

One embodiment of an air pollution detector measures five components ofthe Environmental Protection Agency's Air Quality Index: ozone,particulate matter, carbon monoxide, sulfur dioxide, and nitrous oxide.This device detects all of these pollutants except sulfur dioxide. Thedevice also includes a town gas sensor to alert the user to gas leaks orthe presence of flammable gases. Furthermore, a temperature and humiditysensor is included as these conditions can impact the performance of thegas sensors. The system can also use Shinyei PPD42 Particulate MatterDetector, MQ-2 Gas Sensor, MQ-9 Gas Sensor, MiCS-2714 Gas Sensor (NO2),MiSC-2614 Gas Sensor (Ozone) and Keyes DHT11 Temperature and HumiditySensor to detect air pollution.

City pollution may also impact cloud formation and rainfall. Anelectronic tongue sensor can be provided to sense quality of fog, rainand/or water. The tongue includes a stirring module, a liquid moleculesensor module, a control unit and an output unit. The stirring module isused to pump liquid actively to the liquid molecule sensor module. Themolecule sensor module detects the liquid molecules pumped into by thestirring module. The liquid molecule sensor module at least includes amolecule sensor which is covered with a compound. The compound is usedto combine preset liquid molecules. The control unit controls thestirring module to pump liquid to be “tasted” into the electronic tonguedevice. Then the module transmits a flow current to the liquid moleculesensor module to generate a detected data. The output unit calculatesthe detected data to generate a calculation result and outputs anindicating signal to an operator or compatible host computer accordingto the calculation result. Such electronic tongue can detect quality offog or liquid, among others.

In a method to provide street security, the system obtains dataindicative of the operational status of each street device. In thepresent embodiment, the data is received from the street devices (or themonitoring units connected to, and optionally comprised in, the streetdevices). The data is transmitted (preferably automatically) from themonitoring units (or any unit which has received the data from themonitoring units) to the computing unit. The data may e.g. be airquality, traffic flow, parking availability, gunshot sound, verbalaltercation, sound pollution, light level. The data may also beindicative of a future operational condition of a street deviceestimated (e.g. by the monitoring unit) based on the current operationalstatus of the street device. Further, the data from the street devicesis indicative of the position of each street device 11. The streetdevice may for e.g. send its GPS position. The region description datais obtained for the region in which the street devices are located. Theregion description data may be transmitted (or retrieved) from thedatabase. The region description data may be a (geographic) map (such asa road map) over the region in which the street devices are located. Forexample, the geographic data may be retrieved from the Internet from anon-line map provider. The geographic data may provide information suchas road type (e.g. straight or curved road, roundabout and bridge). Themethod further comprises correlating the geographic positions andoperational statuses of the street devices with the region descriptiondata. For example, the correlation may be provided as a map, table orany other storage/display format pointing out where (at least some of)the street devices are located and indicating their operational status.In the present embodiment, the method may further comprise estimatingtraffic, noise, air pollution, lighting conditions at roads and/or insubareas in the region in which the street devices are located based onthe region description data and the data received from the streetdevices. A processor, GPU or TPU can detect traffic flow, parked car,open parking spot, license plate number, vehicle identification, andface identification. An energy scavenger coupled to the processor tosupply power. A vehicular radio transceiver to communicate with a smartcar. The IoT can be inside an enclosure mounted to a light pole, atraffic light, a government vehicle, a utility vehicle, or a cityvehicle. A cloud-based image processing system can receive images fromthe camera and recognize an image.

The IoT device can run code to minimize light pollution by lighting onlywith a moving person or vehicle in proximity to the light source. Thisis done by detecting motion near each light pole, and turning on only afew lights in the area of motion while keeping the other lights off.This approach has the advantage of shining light on those who hide inthe darkness for nefarious purposes. The IoT device can run code todetect water pipe rupture by recognizing the position of a fire hydrantand when water motion is detected at the hydrant, the IoT device can runcode to report a fire or emergency to a fire department. The IoT devicecan run code to gate off traffic to the fire or emergency. The IoTdevice can run code to detect car accident and request assistance frompolice or ambulance by detecting car collisions or detecting unusualprolonged traffic at a spot. The IoT device can run code to detect crimeusing a combination of video and sound. The IoT device can run code todiscover anomalies with a particular city block. The IoT device can runcode for providing sensor data to a crowd and requesting from the crowdas a game one or more reasons explaining sensor data.

The device can run code to detect sound direction of sound such asgunshot or gang fight or a crime in progress. Because each light pole issequential, the microphone arrays have high resolution and a combinationof microphone data from an array of light poles on both sides of astreet or freeway provides valuable information in detecting sources ofsound, much like SONAR systems. In some embodiments, the sound sourcemay be a natural or an artificial sound generator. Examples of naturalsounds include, without limitation, human sounds, animal sounds,environmental sounds, etc. In this instance, a natural sound generatormay be a human being, an animal, the environment, etc. An example of anartificial sound is a recorded sound, and an artificial sound generatormay be a speaker. The sound wave generated from the sound source andpropagated toward the sound direction detecting module may have aspecific frequency and a certain volume. Further, the sound source maygenerate sound that has distinguishable characteristics (longitudinal ortransverse waves) and physical properties. The characteristics andproperties of a sound wave may also be closely related to thetransmission medium through which the sound wave travels. Further, thegenerated sound may be ultrasound that has a frequency greater than thefrequency that may be detected by a human, or infrasound that has afrequency lower than the frequency that may be detected by a human. Insome embodiments, the sound sensors or microphones may measure thephysical characteristics of the detected sound wave and convert thephysical characteristics into analog or digital signals. The soundsensors may detect the vibration and/or the pressure of the sound wavetraveling through the sound sensors. The microphone arrays or soundsensors of the sound direction detecting module may detect the soundwave generated by the sound source. In some embodiments, the soundsensors are installed on one side of the sound direction detectingmodule and at their respective physical locations. The sound sensor maybe positioned at a physical location different from the sound sensors.For example, the sound sensor may be installed on the opposite side ofthe sound direction detecting module. Thus, the sound sensors may bepositioned to face in a first direction. The sound sensor may bepositioned to face in a second direction, which differs from the firstdirection that the sound sensors face in. In some embodiments, becausethe sound direction detecting module may detect the sound wavepropagated from the sound source in any angle, a distance between thesound sensor and the sound source may be different from a distancebetween the sound sensor and the sound source. Since the intensity ofsound decreases as the distance of propagation increases, the soundpressure detected by the sound sensor is likely to be different from thepressure detected by the sound sensor. On the other hand, if the soundpressures detected by the two sound sensors are substantially identical(same), then the distance and the distance may substantially be thesame. In such a situation, the direction vector of the sound source maybe close to 90 degrees. If the sound wave is not reflected, for example,from some surface, the sound pressures detected from the different soundsensors may be used to show a direction of the sound source relative tothe sound direction detecting module. According to some embodiments ofthe present disclosure, the sound sensors of the sound directiondetecting module may detect the sound wave propagated from analternative sound source, which is different from the sound source. Thesound sensor may have substantially the same distance to the soundsource as to the sound source, and the sound sensor may havesubstantially the same distance to the sound source as to the soundsource. Stated differently, the sound sensor may be positioned orlocated substantially the same distance from the sound source as fromthe sound source, and the sound sensor may be positioned or locatedsubstantially the same distance from the sound source as from the soundsource 140. In this case, the sound direction detecting module may havedifficulty determining whether the direction of the sound wave is fromthe sound source or the sound source if it utilizes the sound pressuresdetected by the sound sensors to determine the direction of the soundwave. Thus, in a two-dimensional space, two sound sensors may be used todetermine a direction vector with approximately 180-degree accuracy.That is, the sound direction detecting module may accurately describe,in angle degrees, whether a sound source is from the left side of, theright side of, or the middle area between the sound sensors in a180-degree range. However, the sound direction detecting module may notbe able to determine whether the sound source is in-front-of or behindthe sound sensors. According to some embodiments of the presentdisclosure, a third sound sensor may be installed in the sound directiondetecting module at a fixed position and on a side of the sounddirection detecting module that is different from the side of the sounddirection detecting module that the sound sensors are located on. Thesound pressure detected by the third sound sensor may then be used tocompare with the pressures detected by the sound sensors in order todetermine whether the sound source is in-front-of or behind the soundsensors. For example, the sound sensor may be placed at a position inbetween the positions of the sound sensors. At the same time, the soundsensor may be placed on a side of the sound direction detecting modulethat is opposite to the side of the sound direction detecting module onwhich the sound sensors are placed. During operation, the distancebetween the sound source and the sound sensor 123 is different. Thus, ifthe sound pressure detected by the sound sensor is weaker than thepressures detected by the sound sensors, it may be reasoned that thesound wave should be from the sound source, which is in front of thesound sensors and has a shorter distance to the sound sensors than tothe sound sensor. Similarly, when the sound pressure detected by thesound sensor is stronger than the pressures detected by the remote soundsensors, the sound direction detecting module may determine that thedistance from the sound source to the sound sensor is shorter than tothe sound sensors. In this case, the sound should be originated from thesound source, which is behind the sound sensors/microphones. Thus, byusing three acoustic sound sensors, the sound direction detecting modulemay divide a two-dimensional plane, into four substantially same-sizedquadrants (front left, front right, behind left, and behind right) fromthe perspective of the sound direction detecting module, and maydetermine a two-dimensional direction vector in a 360-degree range. In asimilar approach, the device can run code to detect air pollution orodor from the electronic nose. The IoT device can run code to detectcrime using a combination of video, odor and sound. Gunshot detectorsbased on video, sound and other IoT sensors help cops guess at theextent of unreported gun crime. With location data, police officersdon't have to spend as much time searching for evidence that a shootinghas occurred, such as spent shell casings. The software can tell whethermultiple guns were used, or whether the shooter was moving as he pulledthe trigger. Camera with face recognition/posture recognition can beturned on to capture events for subsequent analysis.

On each lighting device 11 is a massive MIMO antenna detailed in FIG. 2Chidden into street furniture such as manhole covers, light poles, andreal/fake trees or plants, or even utility poles. As shown therein, thecombined camera, light, sensor, and massive MIMO antenna unit 11 ismounted on a pole which is secured to the traffic pole cross bar viamounts. For example, a fake tree can be used with solar cells on the topof the leaves and the antenna 11 on the top/bottom of the leaves. Theantenna can be near the top of the manhole cover. Referring to FIG. 2C,the street lamp includes one or more sensors 13 (including microphone),a light source 14, a light pervious cover 15, a camera module 16, and alamppost. The light source 14 include a plurality of LEDs (lightemitting diodes). It is understood that the light source 14 can also beincandescent lamps and fluorescent lamps. The light pervious cover islight-permeable. The light beams emitted from the light source 14 aretransmitted through the light pervious cover 15 to illuminate thestreet. A material of the light pervious cover 15 is preferably selectedfrom an anti-reflective material, such as light-permeable plastic, forthe sake of preventing the camera module 16 from interfering by thelight beams reflected within the light pervious cover when picking up animage of the street. The light-permeable plastic may be selected fromthe group consisting of Polymethylmethacrylate (PMMA), Poly Carbonate(PC), silicone, epoxy, polyacrylate. Certainly, the material of thelight pervious cover can also be glass doped with ZnO, B2O3, SiO2, Nb2O5or Na2O. The light pervious cover made of above materials has a lightweight, which is convenient for assembling and disassembling. The cameramodule 16 includes a lens group, a lens and an image sensor. In theexemplary embodiment, the lens group includes two lenses. The cameramodule 16 is configured for capturing the image of the street. Thecamera module 16 can be wire or wireless connected with sectors ofgovernment authorities, e.g. a traffic police. Thus, governmentauthorities can monitor activities on the street via the camera module16 of the street lamp. When an accident happens, the traffic police canget the street information and take action in the accident in time. Theimage sensor 145 can be a charged coupled device (CCD) or acomplementary metal-oxide-semiconductor (CMOS). In use, the cameramodule of the street lamp can capture images of the people and cars onthe street in both bright and dark conditions. In a dark environment,the light source illuminates the street allowing the camera module toclearly capture images of people and cars on the street. The lightsource has an illumination range β defined by a spatial extension whichthe light beams emitted by the light source 14 can reach. The cameramodule has an image field a which the camera module can pick up. Theimage field a of the camera module overlaps the illumination range β ofthe light source. Thus the camera module can capture images of the areawhich the light source 14 illuminates. The light beams emitted by thelight source 14 need to have a high brightness in a bad weather, forexample in foggy weather. FIG. 2D shows a mounted system 12 that doesnot have light source 14, but has camera 16 and antennas 11.

FIG. 2E shows exemplary base station types. In particular, small cellscan include micro cells, pico cells and femto cells. A 5G femtocell is asmall cell designed for use in a home or small business. It is alsocalled femto AccessPoint (AP). It connects to the service provider'snetwork via broadband. A 5G femtocell allows 5G service providers toextend service coverage indoors or at the cell edge, especially whereaccess would otherwise be limited or unavailable. The use of femtocellsallows network coverage in places where the signal to the main networkcells might be too weak. Furthermore, femtocells lower contention on themain network cells, by forming a connection from the end user, throughan internet connection, to the operator's private network infrastructureelsewhere. The lowering of contention to the main cells plays a part inbreathing, where connections are offloaded based on physical distance tocell towers. Owners of the small cells can join a network that pays theowners for access to bandwidth for mobile devices/users. This is ablockchain based 5G system that enables individuals to become a part ofa distributed 5G carrier. In this system, homeowners/businesses own thesmall cells to improve high speed access for occupants. However,conventional femtocells limits access to a few occupants. The system ofFIG. 2E is instead part of a large network where all small cellscontribute to provide high speed 5G access, and owners of the smallcells is paid in a fair manner. The more a member contributes, the morehe/she gets rewarded.

In one embodiment, once users add WiFi passwords, all network users areable to access internet from that WiFi. Default setting in the app isset to share only WiFi but the owner can manually set to share alsomobile plan (3G/4G) internet. By allowing 5G phones to share data, andwith many more smartphones than WiFi hotspots around the world, thesystem allows convenient always available mobile access.

Blockchain is used to ensure fairness without trust. It allows the smallcell networks to form and organize, without a central authority (likeISPs). The blockchain distributed ledgers enable parties who don't fullytrust each other to form and maintain consensus about the existence,status, and evolution of a set of shared 5G performance data. Consumersand small businesses benefit from greatly improved coverage and signalstrength since they have a de facto base station inside their premises.As a result of being relatively close to the femtocell, the mobile phone(user equipment) expends significantly less power for communication withit, thus increasing battery life. They may also get better voice quality(via HD voice) depending on a number of factors such as operator/networksupport, customer contract/price plan, phone and operating systemsupport. Some carriers may also offer more attractive tariffs, forexample discounted calls from home.

In this embodiment, the system is a decentralized 5G network thatenables IOT devices to wirelessly connect to the Internet andefficiently geolocate themselves without the cost and power to run GPSchips. The network has WIFI and 5G transceivers, a blockchain with feesor digital tokens to access the system. Hosts who are providing wirelessnetwork coverage in a cryptographically verified physical location andtime submit proofs to the network. The hosts form a network ofindependent providers that do not rely on a single coordinator, where:(1) Devices pay to send & receive data to the Internet and geolocatethemselves, (2) Hosts earn fees for providing network coverage, and (3)Hosts earn fees from transactions, and for validating the integrity ofthe network.

The distributed ledger stores immutable device data fingerprints, andfurnish a transaction system. The network is an immutable append-onlylist of transactions which achieves consensus using the EthereumProtocol in one implementation, although any other blockchain protocolscan be used.

Hosts earn fees by providing wireless network coverage. Devices storetheir private keys in key-storage hardware and their public keys in theblockchain. Hosts join the network by asserting their satellite-derivedlocation, and staking a fee deposit. Hosts specify the price they arewilling to accept for data transport and Proof-of-Location services, andRouters specify the price they are willing to pay for their Device'sdata. Hosts are paid once they prove they have delivered data to theDevice's specified Router.

Hosts provide wireless network coverage to the network via 5G Hotspots.Routers are Internet applications that purchase encrypted Device datafrom Hosts. In locations with a sufficient number of Hosts, Routers canpay several Hosts to obtain enough copies of a packet to geolocate aDevice, or Proof-of-Location. Routers are the termination points forDevice data encryption. Devices record to the blockchain to whichRouters a given Host should send their data, such that any Hotspot onthe network can send any Device data to the appropriate Router. Routersare responsible for confirming to Hotspots that Device data wasdelivered to the correct destination and that the Host should be paidfor their service.

Hotspots are physical network devices operated by Hosts that createwireless RF coverage over wide areas. Hotspots typically supportmultiple protocols with 5G/Wifi/Bluetooth/Zigbee transceivers, and theytransmit data back and forth between Routers on the Internet and Deviceson the network, process blockchain transactions. Hotspots can connect tothe Internet using any broadband backhaul, such as Ethernet, WiFi orCellular. Hotspots have a GPS or GNSS receiver to obtain accurateposition and date/time information. This satellite-derived location isused in conjunction with other techniques to verify that a Hotspot is,in fact, providing wireless network coverage in the location it claims.Hotspots transmit data back and forth between Routers on the Internetand Devices. Hotspots can co-operate and geolocate Devices using thenetwork without any additional required hardware. Hosts operatingHotspots specify the price they are willing to accept for transport andProof-of-Location services for Devices. The hotspots can also providehardware-based tensor processing units (TPUs) or GPUs to support edge AIprocessing. Such processing can be paid for using fees or tokens.

Routers are Internet-deployed applications that receive packets fromDevices via Hotspots and route them to appropriate destinations. Routersserve several functions on the network, including: authentication,routing packets from hotspots and routing them to the Internet,providing delivery confirmations to ensure transport transactions arehonest, and providing a full copy of the blockchain ledger by acting asa full node

When a hotspot receives a data packet from a Device on the network, itqueries the blockchain to determine which Router to use given theDevice's network address. Anyone is free to host their own Router anddefine their Devices' traffic to be delivered there by any Hotspot onthe network. This ability allows users of the network to create VPN-likefunctionality whereby encrypted data is delivered only to a Router (orset of Routers) that they specify and can optionally host themselves.Any time a device connects to the hotspot, a permanent record of thetransaction is added to the blockchain which can be audited. Recordingtime and location of each transaction enables tracking and otherlocation-based types of use cases. As detailed below, the location canbe determined without power hungry GPS chips.

Devices send and receive encrypted data from the Internet wherefingerprints of the data sent are stored in the blockchain. Devicesspend fees by paying Hosts to send data to and from the Internet.Devices can contain one or more of 5G/Wifi/Bluetooth/Zigbee radiotransceivers and communicate with Hotspots on the network.Zigbee/Bluetooth battery-powered sensors can operate for several yearsusing standard batteries while Wifi and 5G devices provide broadbandspeed at low latency. Satellite location information is also correlatedwith packet arrival events to provide Proof-of-Location for Devices ifmultiple Hotspots observe the same packet. This allows devices to locatethemselves without requiring a GPS/GNSS transceiver physically, andtherefore provide accurate location data at a fraction of the batterylife and cost of competing methods. Devices can exist in a variety offorms, depending on the product or use case, and a variety oftransmission and reception strategies can be employed to optimize fortransmission/reception frequency or battery life. Device manufacturerscan use use hardware-based key storage which can securely generate,store, and authenticate public/private key pairs without leaking theprivate key.

Next, location services for the devices are detailed. Given that anuntrusted source of data used to resolve a digital contract, thecertainty of the data can be increased by first establishing theexistence of a multidirectional proof of location by having multiplenearby wireless nodes validate the occurrence and range of aninteraction by cosigning the interaction. This allows for azero-knowledge proof that the two nodes were in proximity of each other.Analyzing interactions on the chain by every edge node allows the systemto produce the Best Answer from the relative proximity of all the nodesthat are in the network. Given a set of reported data and a query for arelative position of one of the edge nodes, an approximation of theposition can be generated along with coefficients for certainty andaccuracy. Such proof of location is placed on the blockchain. Eachorigin maintains its own ledger and signs it to make an origin chain.Once information on the Origin Chain has been shared, it is effectivelypermanent: the origin generates a public/private key pair, signs theprevious and next blocks with the same pair after including the publickey in both blocks and immediately after the signature is made, theprivate key is deleted. With the immediate deletion of the private key,the risk of a key being stolen or reused is greatly minimized. A seriesof data packets can be chained together by using temporary private keysto sign two successive packets. When the public key paired with theprivate key is included in the data packets, the receiver can verifythat both packets were signed by the same private key. The data in thepacket cannot be altered without breaking the signature, assuring thatthe signed packets were not altered by a third party. The determining ofthe sequence of ledgers is the order in which they were reported. Giventhat it is not possible for a device to change the order of any Originsigned ledger, an absolute order can be established by looking at allthe ledgers collectively.

In one embodiment, the transceivers can operate on unlicensed RF bandusing off the shelf transceivers with MIMO antennas to achieveubiquitous wireless services cheaply and thus can send more data in realtime for more accurate location information at a fraction of the cost ofcellular services. A plurality of devices can form a mesh network toroute information over a large range. Once the data reaches the hotspot,then data can be compressed and sent over broadband connections. Suchinexpensive devices enable low cost asset tracking with pinpointaccuracy.

Devices can execute smart contracts, as detailed in commonly owned U.S.patent Ser. No. 10/195,513, the content of which is incorporated byreference. The smart contracts can be legally enforceable smartcontracts. The system enables Blockchain Internet-of-Things (IoT)commerce.

In one example, mobile users can get access to the internet using thenetwork by paying tokens. In another example, mobile users can accessthe internet by watching a brief ad before data access is granted. Theowner of the hotspot gets a share of the ad spend. The advertiser canselect the audience based on search history, gender, age, social mediapro-le, location (with levels of sophistication for such details asexact street, house or apartment). A hotspot user will focus on the advideo or banner displayed before accessing the Web. The cost of suchadvertising is much lower than that of advertising in search engines orsocial media ads.

In another example, the hotspot owner can verify his/her geolocationthrough a registration with a post card that provides a unique codemailed to the address. When the host or hotspot owner enters the code,the hotspot device location is verified on the blockchain. A pluralityof hotspot locations can vote and authenticate the current location andan IOT device/sensor can rely on such location without power hungry GPSdevices.

In another example, an eCommerce Company offers its premium customerspayment-upon-delivery services. To be able to offer this service, theeCommerce company would write a smart contract (i.e. on Ethereum'splatform). The network could then track the location of the packagebeing sent to the consumer along every single step of fulfillment; fromthe warehouse shelf to the shipping courier, all the way into theconsumer's house and every location in between. This could enableeCommerce retailers and websites to verify, in a trustless way, that thepackage not only appeared on the customer's doorstep, but also safelyinside their home. Once the package has arrived in the customer's home(defined and verified by a specific coordinate), the shipment isconsidered complete and the payment to the vendor gets released. Themerchant/consumer is protected from fraud and ensure consumers only payfor goods that arrive in their home.

In another example, travel reviews are often not trusted. Naturally,hotel owners are incentivized to improve their reviews at any cost. Areview with verified locations would have a very high reputation,especially if it was written by a serial reviewer who has written manyreviews with verified location data.

In another example, an autonomous robot can order electricity orsupplies. In one example, the robot as an energy buyer can send anenergy supplier a transaction and which Energy seller later uses tospend that transaction. The energy buyer spends satoshis to a typicalBitcoin address, and then lets Energy seller further spend thosesatoshis using a simple cryptographic key pair. Energy seller can firstgenerate a private/public key pair before Energy buyer can create thefirst transaction. Bitcoin uses the Elliptic Curve Digital SignatureAlgorithm (ECDSA) with the secp256k1 curve; secp256k1 private keys are256 bits of random data. A copy of that data is deterministicallytransformed into an secp256k1 public key. Because the transformation canbe reliably repeated later, the public key does not need to be stored.The public key (pubkey) is then cryptographically hashed. This pubkeyhash can also be reliably repeated later, so it also does not need to bestored. The hash shortens and obfuscates the public key, making manualtranscription easier and providing security against unanticipatedproblems which might allow reconstruction of private keys from publickey data at some later point. Energy seller provides the pubkey hash toEnergy buyer. Pubkey hashes are almost always sent encoded as Bitcoinaddresses, which are base58-encoded strings containing an addressversion number, the hash, and an error-detection checksum to catchtypos. The address can be transmitted through any medium, includingone-way mediums which prevent the spender from communicating with thereceiver, and it can be further encoded into another format, such as aQR code containing a bitcoin: URI. Once Energy buyer has the address anddecodes it back into a standard hash, she can create the firsttransaction. She creates a standard P2PKH transaction output containinginstructions which allow anyone to spend that output if they can provethey control the private key corresponding to Energy seller's hashedpublic key. These instructions are called the pubkey script orscriptPubKey. Energy buyer broadcasts the transaction and it is added tothe block chain. Energy seller's wallet software displays it as aspendable balance. When, some time later, Energy seller decides to spendthe balance, he must create an input which references the transactionEnergy buyer created by its hash, called a Transaction Identifier(txid), and the specific output she used by its index number (outputindex). He must then create a signature script—a collection of dataparameters which satisfy the conditions Energy buyer placed in theprevious output's pubkey script. Signature scripts are also calledscriptSigs.

Pubkey scripts and signature scripts combine secp256k1 pubkeys andsignatures with conditional logic, creating a programmable authorizationmechanism.

For a P2PKH-style output, Energy seller's signature script will containthe following two pieces of data:

His full (unhashed) public key, so the pubkey script can check that ithashes to the same value as the pubkey hash provided by Energy buyer.

A secp256k1 signature made by using the ECDSA cryptographic formula tocombine certain transaction data (described below) with Energy seller'sprivate key. This lets the pubkey script verify that Energy seller ownsthe private key which created the public key.

Energy seller's secp256k1 signature doesn't just prove Energy sellercontrols his private key; it also makes the non-signature-script partsof his transaction tamper-proof so Energy seller can safely broadcastthem over the peer-to-peer network. The data Energy seller signsincludes the txid and output index of the previous transaction, theprevious output's pubkey script, the pubkey script Energy seller createswhich will let the next recipient spend this transaction's output, andthe amount of satoshis to spend to the next recipient. In essence, theentire transaction is signed except for any signature scripts, whichhold the full public keys and secp256k1 signatures. After putting hissignature and public key in the signature script, Energy sellerbroadcasts the transaction to blockchain miners through the peer-to-peernetwork. Each peer and miner independently validates the transactionbefore broadcasting it further or attempting to include it in a newblock of transactions.

Another embodiment works with Ethereum which is a platform that allowspeople to easily write decentralized applications (Dapps) usingblockchain. A decentralized application is an application which servessome specific purpose to its users, but which has the important propertythat the application itself does not depend on any specific partyexisting. The Ethereum blockchain can be alternately described as ablockchain with a built-in programming language, or as a consensus-basedglobally executed virtual machine. The part of the protocol thatactually handles internal state and computation is referred to as theEthereum Virtual Machine (EVM). From a practical standpoint, the EVM canbe thought of as a large decentralized computer containing millions ofobjects, called “accounts”, which have the ability to maintain aninternal database, execute code and talk to each other.

In one embodiment, the blockchain uses a database called a Patricia tree(or “trie”) to store all accounts; this is essentially a specializedkind of Merkle tree that acts as a generic key/value store. Like astandard Merkle tree, a Patricia tree has a “root hash” that can be usedto refer to the entire tree, and the contents of the tree cannot bemodified without changing the root hash. For each account, the treestores a 4-tuple containing [account_nonce, ether_balance, code_hash,storage_root], where account_nonce is the number of transactions sentfrom the account (kept to prevent replay attacks), ether_balance is thebalance of the account, code_hash the hash of the code if the account isa contract and “ ” otherwise, and storage_root is the root of yetanother Patricia tree which stores the storage data. Unlike Bitcoin,Ethereum blocks contain a copy of both the transaction list and the mostrecent state. Aside from that, two other values, the block number andthe difficulty, are also stored in the block. The basic block validationalgorithm in Ethereum is as follows:

Check if the previous block referenced exists and is valid.

Check that the timestamp of the block is greater than that of thereferenced previous block and less than 15 minutes into the future

Check that the block number, difficulty, transaction root, uncle rootand gas limit (various low-level Ethereum-specific concepts) are valid.

Check that the proof of work on the block is valid.

Let S[0] be the state at the end of the previous block.

Let TX be the block's transaction list, with n transactions. For all iin 0 . . . n−1, set S[i+1]=APPLY(S[i],TX[i]). If any application returnsan error, or if the total gas consumed in the block up until this pointexceeds the GASLIMIT, return an error.

Let S_FINAL be S[n], but adding the block reward paid to the miner.

Check if the Merkle tree root of the state S_FINAL is equal to the finalstate root provided in the block header. If it is, the block is valid;otherwise, it is not valid.

There are two types of accounts:

Externally owned account (EOAs): an account controlled by a private key,and if you own the private key associated with the EOA you have theability to send ether and messages from it.

Contract: an account that has its own code, and is controlled by code.

When a user sends a transaction, if the destination of the transactionis another EOA, then the transaction may transfer some ether butotherwise does nothing. However, if the destination is a contract, thenthe contract in turn activates, and automatically runs its code. Thecode has the ability to read/write to its own internal storage (adatabase mapping 32-byte keys to 32-byte values), read the storage ofthe received message, and send messages to other contracts, triggeringtheir execution in turn. Once execution stops, and all sub-executionstriggered by a message sent by a contract stop (this all happens in adeterministic and synchronous order, ie. a sub-call completes fullybefore the parent call goes any further), the execution environmenthalts once again, until woken by the next transaction.

The distributed ledger or block chain can be used for anonymous energydata analysis and benchmarking, smart grid management, green certificatetrading, energy trade validation, and energy arbitrage among microgridsand main grid.

Smart contracts can be embedded with an if-this-then-that (IFTTT) code,which gives them self-execution. In real life, an intermediary ensuresthat all parties follow through on terms. The blockchain not only waivesthe need for third parties, but also ensures that all ledgerparticipants know the contract details and that contractual termsimplement automatically once conditions are met.

Personal health records can be encoded and stored on the blockchain witha private key which would grant access only to specific individuals andcompliant with HIPAA laws (in a secure and confidential way). Onlyauthorized patients can open and consume prescription drugs. Receipts ofsurgeries can be stored on a blockchain and automatically sent toinsurance providers as proof-of-delivery. The ledger, too, can be usedfor general health care management, such as supervising drugs,regulation compliance, testing results, and managing healthcaresupplies.

The system provides solution in the music industry include ownershiprights, royalty distribution, and transparency. The digital musicindustry focuses on monetizing productions, while ownership rights areoften overlooked. The blockchain and smart contracts technology cancircuit this problem by creating a comprehensive and accuratedecentralized database of music rights. At the same time, the ledger andprovide transparent transmission of artist royalties and real timedistributions to all involved with the labels. Players would be paidwith digital currency according to the specified terms of the contract.The payment for derivative work is automated, and using executablecodes, variations of the music or content can be generated forconsumption based on payment modes.

In one embodiment, an IOT data producer with desirable data advertiseson the blockchain the type of data available and price. To enable this,the producer posts the dataset, or at minimum a description of thedataset to a searchable data store discoverable via a web search or bycommon active marketing activities, such as feeds to targeted potentialdata buyers, advertisements, and so forth. An IOT buyer finds the dataproducer and accepts the terms of the smart contract where the dataitems, the kinds of changes to data items, the scheduling oftransmissions upon changes, and other operational choices are made andagreed to. The data producer and data buyer agree to fees and prices andpayment terms for the originating dataset itself as well as for thechanges to values of data items to be posted to the block chaininfrastructure by the data producer. Micropayments, digital and hardcurrency transactions, and other payment or reward methods for thedataset and the changes in values of data items are communicated usingthe smart contract. The buyer is notified of pending transmission andconsequent transactions can continue until terminated according to thesmart contract. The computer readable code on the device of the databuyer uses the encrypted key with the data value changes in the producerstream and posts them into the relevant data table of the data buyer andthe device of the data buyer initiates or triggers server actions andevents upon confirmation of changes to data values for the data buyer.

The antenna in unit 11 can also work with traditional cell towerantennas, as shown in FIG. 2F. Among other components not shown, theenvironment 100 generally includes a network 102, a base station 104communicatively coupled to a communications tower 106, and anadministrator's computing device 108. The environment 100 might alsoinclude a technician 110 and a mobile device 109. The components of theenvironment 100 may communicate with each other via the network 102,which may include, without limitation, one or more local area networks(LANs), wide area networks (WANs), and any available networkingconfiguration useable to communicate between networked computingdevices. The network might also include telecommunications networks likea public-switched telephone network (PSTN), 2G/3G/4G/5G, Global Systemfor Mobile Communications (GSM), code division multiple access (CDMA),time division multiple access (TDMA), WiFi, Worldwide Interoperabilityfor Microwave Access (WiMAX), or the like. The network may includeprivate or proprietary networks as well as public networks. Suchnetworking environments are commonplace in telecommunicationsindustries, offices, enterprise-wide computer networks, intranets, andthe Internet. A number of administrator computing devices 108, mobiledevices 109, and user devices (not shown), among others, may be employedwithin the environment 100 within the scope of embodiments of theinvention. Each may comprise a single or multiple devices cooperating ina distributed environment. The administrator's computing device 108 andthe mobile device 109 include any computing devices available in the artsuch as a for example a laptop computer, desktop computer, personal dataassistant (PDA) mobile device, or the like. The computing device 108 andthe mobile device 109 include one or more processors, memories, busses,input/output devices, and the like as known in the art. Further detailof components and internal functionality of the computing device 108 orthe mobile device 109 is not necessary for understanding embodiments ofthe invention, and as such, is not described herein. The computingdevice 108 is communicatively coupled to the network while the mobiledevice 109 may be communicatively coupled to the network and/or may becoupled directly, either wirelessly or through a hardwire connection, tothe tower 106. In an embodiment, a plurality of computing devices 108and/or mobile devices 109 is included in the network. The base station104 comprises any components useable to receive, handle, transmit,and/or operate on data received via the network 102 or from componentson the communications tower 106. In an embodiment, the base station 104is a base transceiver station. The base station 104 is configured likebase stations known in the art and thus may include or becommunicatively coupled to components such as a home location registry(HLR), a short-message service center (SMSC), a multimedia messageservice center (MMSC), signal processors, routers, control electronics,power sources, and the like. Further detail of components andfunctionalities of the base station 104 in addition to those describedbelow will be understood by one of skill in the art and are thus notdescribed in detail herein. The data received and transmitted by thebase station 104 over the network and via the components on the tower106 includes voice and/or data communications for transmission to, orreceipt from a wireless communications network by methods known in theart. The data might also include control signaling for operation ofcomponents mounted on the tower 106 as described below. The base station104 is communicatively coupled to components mounted on thecommunications tower 106. The tower 106 includes an antenna mount 113with a plurality of antennas 116 mounted thereon for broadcasting voiceor data signals to a plurality of mobile user devices (not shown) orother receiving units. Any configuration of components necessary fortransmitting signals from the base station 104 through the antennahousings 116 with antennas 11 mounted on the tower 106 may be employedin embodiments of the invention. For example, antenna housings 116 withantennas 11 are associated with one or more radio units 118 and controlunits 120 that may be included in the base station 104 or mounted at thebase or top of the tower 106 with the antenna housings 116 with antennas11. One or more cables 122, wires, fiber-optic lines, or othercommunicative couplings extend from the base station to the tower 106and up the tower 106 to the one or more of the radios 118, control units120, antenna housings 116 with antennas 11, or other components disposedon the tower 106. In an embodiment, a wireless transceiver 124 isdisposed on the tower 106 for wireless communication of one or moresignals to/from the base station 104 or to/from the technician's mobiledevice 109 to one or more of the radios 118, control units 120, antennahousings 116 with antennas 11, or other components mounted on the tower106. In an embodiment, the base station 104 might include a transmitter228 that provides such wireless communications with the transceiver 124.

The tower 106 can comprise any available tower structure known in theart, such as, for example and not limitation, a mast, a tower, a steellattice structure, a concrete reinforced tower, a guyed structure, acantilevered structure, or the like. Or the tower 106 might compriseother structures like a church steeple, a geologic structure, abuilding, or other structure cable of supporting the antenna mount 113of embodiments of the invention described herein.

The antenna mount 113 can be a ring or generally circular structure 126mounted on the tower 106. The ring structure 126 can be mounted at thetop or at any point along the length of the tower 106 and substantiallyencircles the tower 106. One or more spokes 128 extend radially outwardfrom the tower 106 to the ring structure 126 and couple the ringstructure 126 to the tower 106. One or more of the spokes 128 includes apassageway 130 interior to the spoke 128 and traversing the length ofthe spoke 128. The passageway 130 is configured to receive cables 132,wires, fiber optic strands, or other communications components therein.The ring structure 126 is generally circular in shape but may compriseany form or shape that substantially encircles the tower 106. In anembodiment the ring structure 126 only encircles a portion of the tower106. The ring structure 126 has a generally C-shaped cross section thatforms a channel 134 disposed therein that is open to the environmentgenerally along the perimeter of the ring structure 126. The channel 134extends into a body 136 of the ring structure 126.

The linear antenna arrangement is well suited for arrays of radiatingelements feeding the lens, but this arrangement suffers from non-uniformelement spacing when the plurality of radiating elements cover asignificant portion of the lens. The antennas near the edges of theplurality of elements are at a different spacing than the centralelements. The result is non-uniform beam crossover between adjacentradiation beams for the spatial coverage area. For this elementarrangement, a desired minimum beam crossover level is set by the edgeelements where the plurality of remaining elements will certainly meetthe minimum crossover requirements. However, this is predicated on theassumption that the same radiating elements are used for the entireplurality of radiating elements. Otherwise, the beam crossover levelsmay vary across the plurality of radiating elements based on the primaryradiation patterns and illumination efficiency. To overcome the issue ofnon-uniform beam crossover for the linear arrangement of radiatingelements, different element types may be used. For example, dipoleantennas may be used for the outer elements where patch antennas may beused for the central elements. Different antenna types result indifferent primary radiation patterns with different illuminationefficiencies for the lens. The result is a different gain and beamwidthbetween the two antenna types. Therefore, the linear antenna elementarrangement may still be utilized with the same, or nearly the same,beam crossover due to the different element types.

The linear arrangement of the plurality of antenna elements may becombined to form an array with beam steering capabilities. The antennaelements may be combined in azimuth, elevation, or both. The result is afewer number of radiation beams; however, some or all of the beams mayhave steering capability or sidelobe control.

While housing 116 is rectangular in shape, it can be spherical, balloonshape, semispherical, parabolic, inverse parabolic, pyramidal, amongothers. A spherical dielectric lens can provide a multi-beam, high gainantenna system for fifth generation (5G) wireless communications. Thelens is ideally of the Luneburg type lens. To approximate the focusingproperties of the Luneburg lens in a manner that is practical forfabrication purposes, monolithic lenses can be used where the lens iscomprised of a single, homogeneous dielectric material, layered lenseswhere the lens is formed of spherical shells of homogeneous material,and lenses formed by additive or subtractive manufacturing methods wherethe lens dielectric constant is synthesized by voids formed in otherwisesolid dielectric materials. The shells could be connected in anysuitable manner, such as by being bonded together on their touchingsurfaces, or they could be bolted together with non-metallic fasteners.

Objects that have the same shape as each other are said to be similar.If they also have the same scale as each other, they are said to becongruent. Many two-dimensional geometric shapes can be defined by a setof points or vertices and lines connecting the points in a closed chain,as well as the resulting interior points. Such shapes are calledpolygons and include triangles, squares, and pentagons. Other shapes maybe bounded by curves such as the circle or the ellipse. Manythree-dimensional geometric shapes can be defined by a set of vertices,lines connecting the vertices, and two-dimensional faces enclosed bythose lines, as well as the resulting interior points. Such shapes arecalled polyhedrons and include cubes as well as pyramids such astetrahedrons. Other three-dimensional shapes may be bounded by curvedsurfaces, such as the ellipsoid and the sphere. A shape is said to beconvex if all of the points on a line segment between any two of itspoints are also part of the shape. The housing 116 can have any of theseshapes.

Another embodiment uses an active antenna architecture with combinedantenna/radio head with distributed radio functionality across antennaelements. The term fronthaul is used to describe the connection betweenthe cell tower radio itself and the mobile network control backbone (theBaseband Unit or BBU) and CPRI is a well-known standard for thisinterconnection. Backhaul is the linkage between a basestation and thecore wired network, and is often fiber or coax, and in some casesbroadband, proprietary wireless links. Fronthaul, backhaul, and varioushybrid architectures will be needed to accommodate cost efficient,backwards compatible, dense deployment of network infrastructurenecessary to provide the broadband, low latency demands for 5G systems.In one embodiment, a remote fronthaul access point is placed in thecenter of the triangle and communicates with the radio head in theactive antenna via fiber optics or ultrawideband radios.

Another embodiment fuses fronthaul and backhaul into an integrated 5GTransport Network as a flexible, reconfigurable, software definedtransport architecture. A single network is used support a variety offunctional splits between the antenna and the packet core. This alignswith the evolution of Network Function Virtualization (NFV) and CloudRAN (CRAN) which points to the neural network plane or data center thatcan be configured to support whatever functional split is deployed inthe network. At one extreme, a legacy basestation and backhaul can beaccommodated. At the other extreme, a network of densely distributedradio heads configured for massive MIMO can exchange compresseddigitized radio samples for cloud-based processing. 5G-Crosshaul, aEuropean 5GPPP project, can act as a bus/transport network connectingRadio Heads to BBUs which will be virtualized. Once virtualized, basestation functions can be flexibly distributed and moved across datacenters, providing another degree of freedom for load balancing.

Near the tower can be mounted a baseband unit cabinet. The baseband inthe cabinet has a fiber optic output connection using the common publicradio interface (CPRI) protocol and small form factor pluggable (SFP)connectors to fiber. The baseband also has a power output 216 to deliverpower for the active antenna. CPRI fiber extends up the pole or mast tothe active antennas. The antennas are arranged in the figure as fourantennas for each of three sectors. In the active antenna, the radiohead takes the output of the CPRI interface, which is digital, turns itinto an analog radio frequency signal, amplifies it through a PA anddrives the 5G antenna.

Wireless radios may be integrated into the antennas for short-distanceinter-antenna communication. The radios may operate at a high frequency,such as millimeter-wave or 60 GHz, and may be ultrawideband UWB radios.At high frequencies such as used by these radios, high data rates arepossible, sufficient to handle the digital data demands for digitalfronthaul traffic, with minimal interference to the reception andtransmission frequencies of the radios. The wireless range limitationsof frequency bands in the tens of gigahertz (i.e., microwave ormillimeter wave) are not problematic, as the antennas areco-located/mounted on the same radio tower. In some embodiments,backhaul may also be wireless using UWB radios. Backhaul to one antennamay be shared with other antennas, in a mesh network.

A baseband board may be provided to perform all baseband functionsspecific to an antenna. The baseband board may include DPD and CFRfunctions, as well as self-test routines and modules, as well ashandling for one or more channels of MIMO, or one or more channels ofmultiple radio access technologies, e.g., 2G, 3G, 4G, 5G, 6G UMTS, LTE,and the like. At the bottom of the mast, cabinet 421 no longer needs ashelter with air conditioning, as the reduction in power wastage andincrease in thermal mass enables passive cooling at the cabinet.Therefore, no AC and no baseband unit are found at the cabinet; instead,only a passively cooled power supply and a backhaul network terminal areprovided in the cabinet.

In some embodiments, a power tilt antenna chassis may be provided. Insome embodiments, a winch that can lower itself and that causes theantenna to guide itself into position when it is raised can be deployedat the tower in a base or cradle for the antenna module. A drone mayoperate an electric latch to release an antenna module, and the antennamodule may lower itself to the ground using the winch. In someembodiments, a boom and trolley may be attached at the center of a towerfor attaching and detaching antenna modules. The antenna chassis and/orbase may be physically designed to be self-guiding, such that a newantenna may be inserted into the base by a drone or by an operator.

In some embodiments, wireless synchronization may be used betweenantennas. Synchronization is important for various applications, such astime division duplexing (TDD) for certain cellular bands. Directwireless synchronization could be provided or each antenna subsystem maybe equipped with its own GPS antenna, and the GPS antennas may be usedto sync the antennas together down to approximately 50 parts per billion(ppb).

FIG. 2G illustrates a simplified digital baseband beamformingarchitecture that digitally applies complex beamforming weights(composed of both a gain and phase factor) in the baseband domain.Antenna-based communication systems may utilize beamforming in order tocreate steered antenna beams with an antenna array. Beamforming systemsmay adjust the delay and/or gain of each of the signals transmitted by(or received with in the receive direction) the elements of an antennaarray in order to create patterns of constructive and destructiveinference at certain angular directions. Through precise selection ofthe delays and gains of each antenna element, a beamforming architecturemay control the resulting interference pattern in order to realize asteerable “main lobe” that provides high beamgain in a particulardirection. Many beamforming systems may allow for adaptive control ofthe beam pattern through dynamic adjustment of the delay and gainparameters for each antenna element, and accordingly may allow abeamformer to constantly adjust the steering direction of the beam suchas in order to track movement of a transmitter or receiver of interest.

Digital beamformers may employ digital processing in the baseband domainin order to impart the desired phase/delay and gain factors on theantenna array. Accordingly, in digital beamforming systems, the phaseand gain for each antenna element may be applied digitally to eachrespective antenna signal in the baseband domain as a complex weight.The resulting weighted signals may then each be applied to a separateradio frequency (RF) chain, which may each mix the received weightedsignals to radio frequencies and provide the modulated signals to arespective antenna element of the antenna array.

As shown in FIG. 2G, digital beamformer 150 may receive baseband symbols and subsequently apply a complex weight vector pBB=[α1 α2 α3 α4]T to sto generate pBBs, where each element α1, i=1, 2, 3, 4 is a complexweight (comprising a gain factor and phase shift). Accordingly, eachresulting element [α1s α2s α3s α4S]T of pBBS may be baseband symbol smultiplied by some complex weight α1. Digital beamformer 150 may thenmap each element of pBBs to a respective RF chain of RF system 152,which may each perform digital to analog conversion (DAC), radio carriermodulation, and amplification on the received weighted symbols beforeproviding the resulting RF symbols to a respective element of antennaarray 154. Antenna array 154 may then wirelessly transmit each RFsymbol. This exemplary model may also be extended to a multi-layer casewhere a baseband symbol vector s containing multiple baseband symbolss1, s2, etc., in which case baseband precoding vector pBB may beexpanded to a baseband precoding matrix pBB for application to basebandsymbol vector s. In this case, α1, i=1, 2, 3, 4 are row vectors, andpBBs=[α1s α2s α3s α4s]^(T). Thus, after multiplying pBB and s, theoverall dimension is the same as the overall dimension at the output ofdigital beamformer 150. The below descriptions thus refer to digitalbeamformer 150 as pBB and transmit symbol/vector as s for this reasonwhile this model can be extended to further dimensions as explained.

By manipulating the beamforming weights of pBB, digital beamformer 150may be able to utilize each of the four antenna elements of antennaarray 154 to produce a steered beam that has a greater beamgain comparedto a single antenna element. The radio signals emitted by each elementof antenna array 154 may combine to realize a combined waveform thatexhibits a pattern of constructive and destructive interference thatvaries over distances and direction from antenna array 154. Depending ona number of factors (including e.g. antenna array spacing and alignment,radiation patterns, carrier frequency, etc.), the various points ofconstructive and destructive interference of the combined waveform maycreate a focused beam lobe that can be “steered” in direction viaadjustment of the phase and gain factors α1 of pBB. FIG. 2G showsseveral exemplary steered beams emitted by antenna array 154, whichdigital beamformer 150 may directly control by adjusting pBB. Althoughonly steerable main lobes are depicted in the simplified illustration ofFIG. 2G, digital beamformer 150 may be able to comprehensively “form”the overall beam pattern including nulls and sidelobes through similaradjustment of pBB.

In so-called adaptive beamforming approaches, digital beamformer 150 maydynamically change the beamforming weights in order to adjust thedirection and strength of the main lobe in addition to nulls andsidelobes. Such adaptive approaches may allow digital beamformer 150 tosteer the beam in different directions over time, which may be useful totrack the location of a moving target point (e.g. a moving receiver ortransmitter). In a mobile communication context, digital beamformer 150may identify the location of a target User Equipment (UE) 158 (e.g. thedirection or angle of UE 156 relative to antenna array 154) andsubsequently adjust pBB in order to generate a beam pattern with a mainlobe pointing towards UE 156, thus improving the array gain at UE 156and consequently improving the receiver performance. Through adaptivebeamforming, digital beamformer 150 may be able to dynamically adjust or“steer” the beam pattern as UE 156 moves in order to continuouslyprovide focused transmissions to UE 156 (or conversely focusedreception).

Digital beamformer 150 may be implemented as a microprocessor, andaccordingly may be able to exercise a high degree of control over bothgain and phase adjustments of pBB through digital processing. However,as shown in FIG. 1 for RF system 152 and antenna array 154, digitalbeamforming configurations may require a dedicated RF chain for eachelement of antenna array 154 (where each RF chain performs radioprocessing on a separate weighted symbol α is provided by digitalbeamformer 102); i.e. NRF=N where NRF is the number of RF chains and Nis the number of antenna elements.

Hybrid beamforming solutions may apply beamforming in both the basebandand RF domains, and may utilize a reduced number of RF chains connectedto a number of low-complexity analog RF phase shifters. Each analog RFphase shifter may feed into a respective antenna element of the array,thus creating groups of antenna elements that each correspond to aunique RF phase shifter and collectively correspond to a common RFchain. Such hybrid systems may thus reduce the number of required RFchains by accepting slight performance degradations resulting from thereliance on RF phase shifters instead of digital complex weightingelements.

In one embodiment the digital beam former provides a method ofmitigating interference from interfering signals. The system tracks thelocation of interfering signals and readjusts the digital beam formingcoefficients to create nulls in the antenna pattern directed towardsthat interfering signal. The digital beam forming coefficients areadjusted to improve or maximize the signal quality of communicationsignals received from the UEs. The UE provides the cell tower BS withquality indicators which indicate the quality of the signals received bythe UE. In response to received link quality indicators, the digitalbeam former in the BS dynamically adjusts its antenna directionality andthe antenna beam pattern to help optimize the signal transmitted to theUE. The digital beam forming coefficients are readjusted to continuallyhelp maintain and help improve or maximize the signal quality of thereceived signals as the UE and/or the cell tower change their relativepositions. The digital beam former coefficients are adjusted to providemore antenna beams to geographic regions having high demand forcommunication services and also adjusted to provide fewer antenna beamsto regions having a low demand for communication services. In thepreferred embodiment, as the demand for communication services changeswith respect to geographic location, the digital beam former dynamicallyassigns antenna beams or assigns additional beams in response to thechanges in demand for communication services.

In another embodiment the UE receives a link quality indicator from a BS(or another UE) that it is communicating with. The link qualityindicator (LQI) provides preferably 3 data bits indicating of thequality of the signal received at the BS. This link quality indicator isprovided back to BS or UE which accordingly adjusts its transmit digitalbeam forming coefficients dynamically to improve the quality of itstransmitted signal. In this embodiment a local processor, DSP, or aneural network plane evaluates the link quality indicator and adjuststhe beam forming coefficient provided to transmit digital beam formingnetwork. In general this causes the transmit and receive antenna beamcharacteristics to be more optimized for the particular situation the UEis currently experiencing. The situation includes interferencecharacteristics from other signals, interference characteristics causedby ground terrain and the specific receiver antenna characteristics ofthe receiving base station and/or satellite.

In another embodiment the UE tracks the communication signal from thebase station and cell tower as the UE moves. This tracking is done byone of a variety of ways including using the receive signal andanalyzing the angle or direction of arrival of the receipt signal.Alternatively, as the UE moves, the antenna beams, preferably bothtransmit and receive, are continually adjusted to help improve signalquality. Accordingly, the resulting antenna beam patterns are directedtowards the communication station, while nulls are directed toward anyinterfering signal source. As the UE moves (or the small cell/cell towermoves), the antenna beam characteristics, through the use of the digitalbeam former, are adjusted to maintain improved communication with the BSand preferably remain directed towards the BS as the BS moves relativeto the UE or vice versa.

Digital beam former of FIG. 2G provides for positioning of nulls in theantenna beam pattern and provides for beam shaping and other beamcharacteristics that are dynamically modified through the use of thesedigital beam forming techniques. In a preferred embodiment, the digitalbeam former provides dynamically reconfigurable antenna patterns basedon current traffic demand levels. For example, one antenna beam providesbroad coverage over a large region having a low demand for communicationservices, while other antenna beams are small and provide a highconcentration of communication capacity in a region having high demandfor communication services. In another embodiment, antenna beams areshaped in responsive to demand for communication services. Antenna beamsare modified and shaped, for example, to approximate the contour of ageographic region having high demand for communication services next toan area having virtually no demand for communication services.Accordingly, communication capacity may be concentrated where it isneeded. In the preferred embodiment, antenna beams are dynamicallyconfigured in real time in response to demand for communicationservices. However, in other embodiments of the present invention,antenna beams are provided based on historic and measured demand forcommunication services.

In one embodiment, the UE listens for signals, preferably within thesmall cell's footprint. Preferably, receive beam controller moduleconfigures the antenna beams to provides at least one broad antenna beamcovering substantially an entire small cell footprint. Accordingly,signals are received from anywhere within that footprint on that oneantenna beam. Signals that are received may include signals fromexisting users that are already communicating with the small cellsystem, interfering signals, e.g., signals from non-system usersincluding interfering signals, and signals from system users requestingaccess to the system.

The neural network plane determines whether or not the signal is onefrom an existing user. In general, the location of existing users isknown. If the signal received is not from an existing user, the systemdetermines the location of that signal source. Those of skill in the artwill recognize that various ways may be used to determine the geographiclocation of a signal source. Those ways may include analyzing the angleof arrival, the time of arrival, frequency of arrival, etc.Alternatively, if the signal source is a user requesting system access,that UE may provide geographic coordinates on its system access requestsignal.

Once the location of the signal source is determined the systemdetermines whether or not the signal is an interfering signal. In otherwords, the system determines if the signal source will interfere with aportion of the spectrum assigned to the small cell system, oralternatively, if the interfering signal is a communication channelcurrently in use with a UE communicating with the small cell. If thesystem determines that the signal source is not an interfering signaland that the signal source is a request for a new channel, the systemassigns an antenna beam to that user. The system may employ varioussecurity and access request procedures which are not necessarilyimportant to the present description. Beam control modules then generateindividual receive and transmit antenna beams directed to that UE atthat UE's geographic location. The system preferably, repeatedly adjuststhe DBF transmit and receive coefficients to help provide improvedsignal quality received from the UE.

In one preferred embodiment of the present invention the UE provides alink quality indicator (LQI) that indicates the quality of the receivedsignal. The UE provides that link quality indicator to the small cell.The link quality indicator is evaluated causing transmit beam controlmodule to adjust DBF control coefficients to help optimize thetransmitted antenna beam to the UE.

When the system determines that the signal source is an interferingsignal, for example a non-system user, the system calculates and adjustthe receive DBF coefficients provided to receive DBF network 32 to helpreduce or minimize interference from the interring signal. In oneembodiment of the present invention, the system 118 places a “null” inthe antenna pattern in the direction of the interfering signal. Theinterfering signal is continually monitored and tracked as either the UEmoves or the interfering signal moves.

When the system has determined that the signal source is an existinguser, the system determines when a hand-off is required. In someembodiments of the present invention the UE requests hand-offs while inother embodiments, the neural network plane determines when a hand-offis necessary. Preferably, hand-offs are determined based on signalquality. In general, a hand-off is requested when a user is near theedge of the antenna pattern footprint region or exclusion zone.

In one preferred embodiment of the present invention, antenna beams areindividually provided to the UE and the individual antenna beam tracksthe location of the UE. Accordingly, hand-offs are only between smallcells and necessary at the edge of the small cell footprint. When ahand-off is necessary, the system assigns a new antenna beam fromanother small cell to the user. If a hand-off is not required, in-bandinterference is monitored along with received power level and linkquality metrics.

In the system 132, the receive and transmits digital beam former (DBF)coefficients are adjusted to help maintain an improved or maximum signalquality, to help reduce or minimize in-band interference and to helpmaximize receive power level. During this “tracking” mode, additionalinterfering signals may cause a degradation in signal quality.Accordingly, the system dynamically readjusts the DBF coefficients tohelp maintain signal quality. In one embodiment of present inventionlink quality indicators are provided by BSs or UEs. Accordingly, thecombination provide for tracking of the UE as the relative locationbetween the UE and the small cell change. The system 34 determines whena hand-off is required. If a hand-off is not required the UE remains inthe tracking mode. When the hand-off is required the system will executea hand-off to the next small cell. In one embodiment of the presentinvention the next small cell is notified that a hand-off is requiredand it is provided the geographic location of the UE. Accordingly, thenext small cell can assign and generate an antenna beam specifically forthat UE before being released from its present small cell. Once the UEis handed off to the next small cell, the system adds the availableantenna beam to its resource pool, allowing that antenna beam to beavailable to be assigned to another UE.

In another embodiment, the neural network plane determines the locationof high demand and low demand geographic regions and this can beaccomplished in any number of ways. For example, each UE communicatingwith the system has a geographic location associated therewith.Furthermore, each UE requesting access to the system may provide thesystem with geographic location data. Once the geographic locations ofhigh demand and low demand areas are determined, the system causes theDBF beam control modules to provide less antenna beams in low demandareas and provide more antenna beams in high demand areas. In oneembodiment of the present invention, each antenna beam provides alimited amount of communication capacity.

Low demand areas are provided with antenna beams having a much largercoverage region than antenna beams being provided to high demand areas.For example, antenna beam covers a large geographic region thatcurrently has a low demand for communication services. Alternatively,antenna beams have much smaller geographic coverage regions and providemore communication capacity for a region that currently has a highdemand for communication services. In another embodiment of the presentinvention the systems adjust the shape of the antenna beams based on thedemand for communication services. For example, antenna beams can belong narrow beams formed to provide better area coverage forcommunication services.

As the demand for communication services changes, antenna beams aredynamically provided in response. As the day begins, antenna beams areinitially at homes. As the day progresses, the antenna beams transitionto office locations as the time of day changes in response to demand forcommunication services. In the case of a natural disaster where demandfor communication services may be particularly great, dedicated antennabeams may be provided. A small cell control facility may direct smallcell's digital beam former to allocate beams accordingly. In general,antenna beams preferably are provided in response to the changing demandof communication services using the neural network plane without theassistance of operators.

FIG. 2H-21 shows an exemplary active antenna system (AAS) and a remoteradio head (RRH) connected to a baseband unit with a high-speed seriallink as defined by the Common Public Radio Interface (CPRI), Open BaseStation Architecture Initiative (OBSAI), or Open Radio Interface (ORI).The high speed serial link is used to transport the Tx and Rx signalsfrom the BBU to the RRH or AAS. In an RRH, the downlink (Tx) signal isdigitally upconverted and amplified on the downlink path.Correspondingly the analog uplink (Rx) signal is processed by a lownoise amplifier (LNA), downconverted and digitized. The duplexed outputsfrom the RRH feed a passive antenna array via a corporate feed networkwith RET support. The RRH comprises two transceivers, one for each MIMOpath. Each transceiver incorporates an upconverter, an amplifier, anLNA, a downconverter, and a duplexer.

In an active antenna, each element in the antenna array is connected toa separate transceiver element. A typical AAS system may therefore havemultiple transceivers (for example 8-16). Since there are many moretransceivers/amplifiers in an AAS, each amplifier in an AAS delivers amuch lower power when compared to an amplifier in an equivalent RRH. Thebenefits of AAS over an RRH based site architecture include: sitefootprint reduction, distribution of radio functions within the antennaresults in built-in redundancy and improved thermal performance, anddistributed transceivers can support a host of advanced electronicbeam-tilt features that can enable improvements in network capacity andcoverage. The integration of the radio within the antenna is theelimination of components like cables, connectors, and mounting hardwareand an overall reduction in the physical tower space required. Byintegrating the remote radio head functionality into the antenna, theaesthetics of the site can be improved and wind load reduced, resultingin lower leasing and installation costs.

The active antenna architecture can eliminate a substantial portion ofthe power losses in the RF feeder cables when compared to a conventionalBTS. Additionally, the active antenna can support an electronic beamtilt without requiring a Remote Electrical Tilt (RET) feeder network.This further reduces the power loss for an AAS when compared to an RRHwith a RET. In most configurations this can increase the power deliveredto the antenna when compared with an RRH. The additional margin can beused to lower the overall thermal dissipation in the amplifiers.

Further, with the radios integrated directly into the antenna housing,and with replacement of a small number of large amplifiers with manysmall amplifiers, the heat is spread over the larger antenna structureas opposed to the smaller RRH or amplifier shelf. This availability ofhigher surface area for heat dissipation results lower temperature risesin the components, which results in improved thermal margins and betterreliability.

The distributed and redundant architecture of the AAS, wherein eachantenna element is fed by its own transceiver, provides reliabilitybenefits as the failure of one transceiver does not cause a criticalfailure. The system is intelligent and can sense a transceiver failure.When a transceiver does fail, the amplitude and phases on the remainingelements are automatically adjusted digitally to compensate for theelevation beam distortion and the reduction of EIRP on the horizon. Withthe appropriate sizing of the amplifiers and intelligent readjustment ofthe amplitudes and phases, the AAS can be designed to have minimal or noloss in coverage performance with a single transceiver failure andminimal degradation with two transceiver failures. Since the likelihoodof more than one transceiver failing in a single AAS is minimal, veryhigh system availabilities can be achieved.

Since the AAS can be designed to have minimal loss in performance with asingle transceiver failure, repairs and site upgrades for failed unitscan be delayed and scheduled. For a site with several sectors and bands,multiple unscheduled repair visits (as would be the case for an RRHbased system) can be replaced by a single scheduled visit that is lessfrequent. This can significantly reduce the operational costs foroperators.

The AAS can electronically tilt elevation beams by having independentbaseband control of the phase, amplitude, and delay of individualcarriers on each antenna element. This supports multi-mode systems wheredifferent carriers in the same frequency band, with different airinterfaces, may require different tilt orientations. The flexibilitywith tilt control in AAS enables advanced RF planning features, much ofwhich can potentially reduce the cost to operators by reducing thenumber of sites required. The electronic tilt capability also allows forthe separate beam tilting and optimization of the Tx (downlink) and Rx(uplink) paths for cases when the link budgets for the Tx and Rx pathsare unequal. It may also be used to optimize cell radii when thephysical layer (modulation scheme) for the Tx and Rx paths is different,as is the case with LTE. Tilt can be adjusted on a per-carrier basis.This can be used vertical sectorization in LTE and RAN sharing for UMTS.In UMTS/LTE networks, adding sectors in the vertical plane can be donewhere the first carrier may cover an inner sector whereas a secondcarrier covers an outer sector.

As multiple operators vie for precious real estate on tower tops,antenna sharing and RAN sharing amongst two or more operators can bedone. The RAN that supports a multicarrier UMTS system is shared by twooperators with each operator controlling/owning one or more of theindividual carriers. Since the RF planning and site deployments arelikely to differ among operators, each UMTS carrier may need to betilted by different amounts in order for each operator to achieveoptimal network performance and optimizing beam tilt on a per-carrierbasis based on active channel loading using Self-organizing networks(SON) algorithms can provide even higher network efficiencies.

FIG. 2J shows exemplary vehicles that can be used to supplement 5Gservices as mobile 5G cell towers. For example, drone arrays can be setup to beam signals to client devices and carry 5G active antennas toallow the BS to communicate with the UEs. On the air,balloons/planes/helicopters, and even LEOs can be used to provide radiocommunications to the client devices with the 5G active antennas toallow the BS to communicate with the UEs. On the ground,trucks/buses/vans/cars can provide mobile 5G radio support. Such groundvehicles can use elevatable antenna that extends to increase height tothe 5G active antennas to allow the BS to communicate with the UEs.

In one embodiment, a hybrid lighter than air/heavier than aircraft orair vehicle can be used as a Geostationary balloon satellites (GBS) areatmosphere analogues to satellites at a fixed point over the Earth'ssurface and the GBS can carry 5G active antennas to allow the BS tocommunicate with the UEs. In one embodiment, the lighter than air gascan be helium to ascend, and an airbag that compresses air to allow thedrone to descend. Alternatively air can be liquified using ultra lowtemperature refrigeration such as LN2 cryogenic refrigeration. Solarcells provide energy for the GBS, and the hybrid air propulsion systemspends about half of its time as heavier than air and half of its timeas lighter than air vehicle to provide propulsion using variablebuoyancy propulsion to allow the balloon to move into and maintain itsposition with minimal power consumption. In another embodiment, inaddition to solar panels the GBS can receive laser power from a grounddevice that the GBS hovers over. Antennas would be auto-steered to aimdirectly at UEs they communicate with. In yet another GBS embodiment, anautonomous variable density, variable volume articulated aircraft has anaircraft body including a section defining a contractible and expandableaircraft body in cross section, a storage tank fixed to the aircraftbody, a mass of first gas having a density less than air within one ofthe chambers, a medium for readily absorbing large masses of the firstgas within the storage tank to appreciably reduce the volume of the onechamber carrying the gas, the amount of the first gas and the absorbingmedium being sufficient to permit a change in density of the aircraftfrom lighter than air to heavier than air and vice versa, a pump totransporting the first gas from the one chamber to the tank absorptionthereof, and a pump for selectively driving the gas from the absorbingwithin the tank to the one chamber for increasing the volume of gaswithin the compartment and the size of the aircraft body and reductionin density of the aircraft. In one embodiment, the medium for absorbingthe first gas comprises water, the aircraft further comprising conduitfluid connecting the one chamber to the storage tank, pump providedwithin the conduit means for pumping gas from the one chamber to thestorage tank and the conduit terminating in a gas diffuser within thetank submerged within the water. One embodiment drives the gas from theabsorbing medium with a heater operatively positioned with respect tothe tank for heating the solution formed by the water absorbing thefirst gas to release the gas from the liquid.

In another aspect, a drone can be used to supply the GBS with power. Inone embodiment, the drone can swap battery with the GBS. In thisembodiment, the GBS has a plurality of energy sources including at leastone battery port or chamber having a latch to secure the battery to theGBS. A drone brings up a battery unit near the battery port of thedrone, unlatches or unscrews the depleted battery and stores thedepleted battery into a chamber. Lowering the battery can disconnect oneor more couplings. One or more other disconnects can be used in someimplementations. For example, separate quick disconnects can be used forrespective high-voltage connection, low-voltage connection and a coolantconnection. When the battery is successfully mounted onto the GBS, anyquick disconnects on the GBS are then properly connected withcorresponding disconnects on the new battery pack. This can ensureproper connection of high voltage, low voltage and liquid coolant to theGBS. For example, the GBS's internal system can check whether there isany water intrusion into the battery pack, or whether there are anyshort circuits. A replacement battery is then positioned in the exposedbattery chamber, and arms on the drone secure the latch to seal thebattery chamber. The refueling drone can detach from the GBS and goes tothe next battery to be swapped on the GBS, and if done, the drone canreturn to a home station.

In another embodiment, the GBS is powered by hydrogen fuel cells, andthe drone can refuel the GBS with gas or hydrogen fuel. Prior to flyingto the GBS to refuel it, the internal hydrogen storage tanks in therefueling drone must be filled. A hydrogen storage subsystem is providedwithin the transportable hydrogen refueling station to refill or chargethe lightweight composite hydrogen storage tanks, a quick connect, whichcan be any standard hydrogen connector, is used to connect an externalhydrogen source to hydrogen storage subsystem. Downstream from the quickconnect is a pressure release valve. The pressure release valve is asafety element to prevent hydrogen, at a pressure exceeding apre-determined maximum, from entering the hydrogen storage subsystem. Ifthe pressure of hydrogen being introduced through the quick connectexceeds a safe limit a restricted orifice working in combination with apressure relief valve causes the excess hydrogen to be vented through avent stack. In general, the valves are used to affect the flow ofhydrogen within the refueling station. A check valve, between the ventstack and pressure relief valve, maintains a one way flow of the flow ofpressurized hydrogen being relived from the storage subsystem. Therestrictive orifice also prevents the hydrogen from entering thepressure rated feed line at a rate which causes extreme rapid filling ofthe lightweight hydrogen storage tanks. Prior to connecting the quickconnect nitrogen gas, or other inert gas can be introduced into the feedline to purge any air from the feed line. Pressurized nitrogen dispensedfrom a nitrogen tank can be introduced through a nitrogen filling valve.One or more hydrogen leak sensors are also distributed and connected tothe system controller. The pressure of the gaseous hydrogen is measuredby one or more pressure sensors placed in the feed line. The firstcompressor subsystem contains an oil cooled first intensifier. An oil toair heat exchanger for cooling hydraulic oil which is supplied to afirst intensifier heat exchanger to cool the first intensifier. Theintensifier is a device, which unlike a simple compressor, can receivegas at varying pressures and provide an output stream at a near constantpressure. However, it may be suitable in some cases to use a compressorin place of an intensifier. The pressure of gaseous hydrogen whichenters a second compressor subsystem at about 4,000 psi can be increasedto achieve the desired 10,000 psi. The system controller can be used tomaintain balance during the refilling of the lightweight compositehydrogen storage tanks by monitoring the pressure of each of thelightweight composite hydrogen storage tanks via adjacent pressuresensors. The system controller, in turn can switch between storage tanksand select which tank to fill at a given time interval during thefilling.

The refueling drone can be used for refueling from the high pressuretanks. The hydrogen fueling subsystem is used to refuel an externalhydrogen storage vessel in the GBS with pressurized hydrogen from therefueling drone. As the refueling begins after the system controllerwill check pre-identified parameters, such as, temperature and pressureof the external hydrogen storage vessel, confirmation of groundconnection and in some cases, confirmation from vehicles of readiness tofill, in order to determine whether hydrogen should be dispensed to theexternal hydrogen vessel. The actual hydrogen refueling process can bepreceded by safety measures. Pressurized nitrogen, or other inert gas,may be introduced through a purge line into the hydrogen dispensing feedlines to purge any air from the hydrogen dispensing feed lines. Thepurge is to manage the risk of dangerous hydrogen-air (oxygen) mixturesbeing formed and or being supplied to the external hydrogen vessel.Purge pressure relief valves are appropriately located to vent gas fromthe hydrogen dispensing feed lines. One proposed industry standard for afuel cell vehicle fill coupler is described in the proposed “FuelingInterface Specification” prepared by the California Fuel CellPartnership that description which is hereby incorporated by reference.The fill coupler, indicated in the proposed “Fueling InterfaceSpecification”, has a “smart” connect which, among other parameters,checks the pressure, temperature and volume of hydrogen within the tanksof a vehicle 12 (the external hydrogen storage vessel 25) beingrefueled. It will also check that the vehicle is grounded. The “smart”fill coupler can communicate with the refueling drone and after theexternal hydrogen vessel and the fill coupler are connected, rechargingor filling of the hydrogen receptacle can occur. When refueling orrecharging an external hydrogen storage vessel preferably a map of theexternal hydrogen vessel should be obtained. A map should check thetemperature, volume and pressure of the hydrogen gas in the externalhydrogen vessel and the volume pressure and temperature of the hydrogenin each lightweight composite hydrogen storage tanks and the map mayinclude information about the pressure rating and capacity of theexternal hydrogen vessel. By controlling the temperature of the hydrogengas during refueling a faster refueling can take place. If thetemperature of the hydrogen in the external hydrogen vessel increasepast ambient the volume of hydrogen which the external hydrogen vesselcan store is decreased. Temperature management supports fasterdispensing of dense gaseous hydrogen.

Preferably, the refueling drone designed for boom-type transfers inwhich a boom controller extends and maneuvers a boom to establish aconnection to transfer hydrogen fuel from the refueling drone to therefueling drone. Prior to refueling, the refueling drone extends arefueling probe. The refueling probe 106 when fully extended, may belong enough for the refueling drone to safely approach and connect tothe refueling probe. The distal end of the refueling probe connects to areceptacle 108 on an exterior of the refueling drone.

The refueling drone needs to be able to maneuver into position foraerial refueling and maintain its position during the refueling. Therefueling drone includes a navigation system that may be used forpositioning the refueling drone during aerial refueling. The GBSnavigation system provides inertial and Global Positioning System (GPS)measurement data to the refueling drone via a data link. The navigationsystem then uses the inertial and GPS data for both the refueling droneand the GBS to compute a relative navigation solution, otherwisereferred to as a relative vector. Preferably, the relative navigationsolution is a GPS Real-Time Kinematic (RTK)/INS tightly coupled relativenavigation solution. The relative navigation solution is calculatedbased on what data is available to the navigation system and allows theGBS to accurately and confidently maintain its relative position to therefueling drone. The navigation system includes an Inertial NavigationSystem (INS), a GPS, a navigation processor, and a system processor. Thenavigation system may have other sensors, such as magnetometer, an airdata computer, and antennas for the data link and the GPS sensors. TheINS may provide acceleration and angular rate data for the refuelingdrone. The refueling drone may include a similar INS for generatinginertial data to be transmitted to the refueling drone. Typically, theINS relies on three orthogonally mounted acceleration sensors and threenominally orthogonally mounted inertial angular rate sensors, which canprovide three-axis acceleration and angular rate measurement signals.For example, the INS may include three accelerometers and threegyroscopes. The three accelerometers and three gyroscopes may bepackaged together with a processor, associated navigation software, andinertial electronics. The inertial electronics may be used to convertthe acceleration and angular rate data into a digital representation ofthe data. The type of relative navigation solution provided by thesystem processor depends on the type of data available to the systemprocessor. The relative position may be a simple difference of theplatform (i.e., the GBS and the refueling drone) reported PVTs of auniquely derived integrated relative GPS/INS solution. The types ofplatform navigation solutions include a GPS-only solution, a looselycoupled GPS/INS solution, and a tightly coupled GPS/INS solution thatincorporates any combination of the other solutions. In addition to theplatform PVT solutions, measurement data from both platforms may alsoavailable and be used to compute the relative solution independently ofthe PVT solutions being provided by each platform. It is important tonote that the relative navigation solution is not limited to thesesolutions. For example, the relative navigation solution may also be anultra-tightly coupled solution. The relative vector is calculated usingthe available data and processing techniques. A fixed solution ispossible when a double difference (DD) process is able to confidentlyresolve the carrier phase DD integer ambiguities. A float solution isavailable when there exists five or more common sets (i.e., common tothe GBS and the refueling drone) of GPS pseudorange and carrier phase.Relative GPS (RGPS) refers to a GPS-based relative solution that doesnot take into account the inertial measurement data from either the GBSor the refueling drone. Coupled or blended solutions integrate theavailable data (both GPS and INS) to form a relative vector between theGBS and the refueling drone. Depending on the distance between therefueling drone and the GBS, and the data link message content, therefueling drone selects the best available solution for relativenavigation. The required level of performance, in terms of accuracy andintegrity, is a function of the level of safety required for navigation.In general, the closer the GBS is to the refueling drone, the moreaccurate the relative navigation solution should be to avoid anunanticipated collision, while maintaining the refueling position. Theprotection levels associated with the relative vector are a function ofthe type of measurements available for processing and the confidence inthose measurements from the GBS to the refueling drone. The protectionlevels associated with the relative vector may also be a function of therange from the GBS to the refueling drone. With multiple sets ofmeasurement data, it is possible to calculate several relativenavigation solutions. For example, if the refueling drone has three EGIsystems on board and the GBS has two EGI systems on board, the systemprocessor may form up to thirty independent relative navigationsolutions. The multiple navigation solutions may be compared. If one ormore of the navigation solutions is not consistent with the othernavigation solutions, the system processor 208 may discard theinconsistent relative navigation solutions. In this manner, the failureof a GPS receiver and/or an inertial sensor may be detected and isolatedby the system processor 208. A threshold for identifying inconsistentnavigation solutions may be adjusted based on the requirements of aerialrefueling. Aerial refueling requirements may be set by one or moreregulatory agencies.

In one embodiment, a plurality of relative navigation solutions iscalculated by the system processor. A flock of bird approach may beused. The type of relative navigation solution can vary based on thedata available to the system processor. The number of relativenavigation solutions calculated depends on the number of EGI systems onboard the GBS and the refueling drone, and the currently available datafrom each sensor. Preferably, each of the solutions has the samebaseline (assumes lever arms between EGI systems and accompanying GPSantennas). Next, the relative navigation solutions are compared witheach other. The comparison detects whether any of the relativenavigation solutions is inconsistent with the other solutions. Aninconsistent solution may be an indication that one or more of the GPSreceivers and/or inertial sensors is malfunctioning. The consistencyinformation may be used to form a protection level for the relativenavigation solution. The relative navigation solutions are compared to athreshold, such as the protection level determined by the consistencyinformation. At block 308, if a particular relative navigation solutionexceeds the threshold, the system processor 208 discards the solution.Otherwise, at block 310, the solution is used to navigate the refuelingdrone during aerial refueling. As a result, the refueling drone maysafely and efficiently rendezvous with the GBS for aerial refueling.

In another embodiment, a kite can be tethered to a ground station andcarry 5G active antennas to allow the BS to communicate with the UEs.The kite can carry propeller engines to provide propulsion if needed.The cable tethering the kite to the ground station supplies power andfiber optic broadband communication for the 5G active antennas to allowthe BS to communicate with the UEs.

In another aspect, a moveable vehicle including a pole and a top portionto mount 4G antennas and a 5G housing, wherein the pole is retractableand extendable during 5G operation; and one or more antennas mounted onthe 5G housing and in communication with a predetermined target using 5Gprotocols.

In another aspect, a system includes an airborne frame to mount 4Gantennas and a 5G housing; and one or more antennas mounted on the 5Ghousing and in communication with a predetermined target using 5Gprotocols.

The system may include one or more of the following:

-   -   A processor can control to change the curvature of the surface        and/or to change the directionality of the antenna.    -   The processor can calibrate the RF link between the tower and        the client device.    -   The processor can calibrate the connection by examining the RSSI        and TSSI and scan the moveable lens until the optimal RSSI/TSSI        levels (or other cellular parameters) are reached.    -   The scanning of the target client/device can be done by        injecting or removing liquid from moveable surface, or can be        done by moving actuators coupled to the surface.    -   Opposing pairs of lenses can be formed to provide two-sided        communication antennas.    -   An array of actuator/antenna can be used (similar to bee eyes),        each antenna is independently steerable to optimize 5G        transmission.    -   Fresnel lens can be used to improve SNR.    -   The focusing of the 5G signals to the target client/device can        be automatically done using processor with iterative changes in        the orientation of the antenna by changing the curvature or        shape of the surface until predetermined criteria is achieved        such as the best transmission speed, TSSI, RSSI, SNR, among        others.    -   A learning machine such as neural network or SVM can be used        over the control/management plane of the 5G network to optimize        5G parameters based on local behaviors.    -   A movable surface can be provided on the housing to steer the        antenna. The moveable surface can be liquid lens or actuator        array as described above.    -   Cameras and sensors can be positioned to capture security        information.    -   Learning machine hardware can provide local processing at the        edge.    -   The air frame has an antenna support structure having means to        permit its collapsing and a waveguide antenna mounted to said        support structure and including a plurality of integrally        connected tubular waveguide cells that form a cell array that        focuses transmitted signals onto a signal processing device;        said lens waveguide antenna having means to permit its        collapsing and a second support structure mount that operatively        connects said collapsible support structure to a mounting        surface to correctly position said collapsible lens waveguide        antenna relative to said signal processing device when said        antenna is operationally deployed.    -   A fleet of drones can operate and navigate as a flock of birds        to provide real time adjustment in coverage as needed. The flock        of birds antenna has power and autonomous navigation and can        self-assemble and scatter as needed to avoid physical and        wireless communication obstacles.    -   The cars/trucks/buses can carry ads as a monetization system.        Alternatively, personal vehicles can be paid a percentage of the        traffic relayed by their vehicles.

Turning now to the details of the antenna that converts electriccurrents into electromagnetic waves and vice versa, the antenna can beconsidered a complex resistive-inductive-capacitive (RLC) network. Atsome frequencies, it will appear as an inductive reactance, at others asa capacitive reactance. At a specific frequency, both reactances will beequal in magnitude, but opposite in influence, and thus cancel eachother. At this specific frequency, the impedance is purely resistive andthe antenna is said to be resonant. The frequency of the electromagneticwaves is related to the wavelength by the well-known equation λ=c/f,where f is the frequency in hertz (Hz), λ is the wavelength in meters(m), and c is the speed of light (2.998×108 meters/second). Sinceresonance will occur at whole number fractions (½, ⅓, ¼, etc.) of thefundamental frequency, shorter antennas can be used to send and recoverthe signal. As with everything in engineering, there is a trade-off.Reducing the antenna's size will have some impact on the efficiency andimpedance of the antenna, which can affect the final performance of thesystem. A half-wave dipole antenna has a length that is one-half of thefundamental wavelength. It is broken into two quarter-wave lengthscalled elements. The elements are set at 180 degrees from each other andfed from the middle. This type of antenna is called a center-fedhalf-wave dipole and shortens the antenna length by half. The half-wavedipole antenna is widely used as it cuts the antenna size in half whileproviding good overall performance. The dipole antenna can have one ofthe quarter-wave elements of a dipole and allow the ground plane on theproduct's pc board to serve as a counterpoise, creating the otherquarter-wave element to reduce size. Since most devices have a circuitboard, using it for half of the antenna is space efficient and can lowercost. Generally, this half of the antenna will be connected to groundand the transmitter or receiver will reference it accordingly. Thisstyle is called a quarter-wave monopole and is among the most commonantenna in today's portable devices. Another way to reduce the size ofthe antenna is to coil the element. This is where the straight wire iscoiled or wrapped around a non-conductive substrate to create a helicalelement. This has the advantage of shortening the apparent length, butit will also reduce the antenna's bandwidth. Like an inductor, thetighter the coil and the higher the Q, the smaller the bandwidth.

It is stressed, however, that the present system is not limited todipole elements, but rather any suitable structure can be utilized.Crossed dipoles are used in many mobile base station antennas to provideorthogonal, dual linear polarization for polarization diversity. Thelens may be fed by any style of radiating antenna element such as thepatch antenna, open-ended waveguide antenna, horn antenna, etc.Generally, low gain antennas are selected as feed elements for thespherical lens in order to maximize the lens efficiency and thedirectivity of the secondary radiation beam. The present invention isalso capable of operating with multiple polarizations thanks to thespherically symmetric nature of the dielectric lens. The radiatingantenna elements may exhibit single linear, dual linear, or circularpolarization. Multiple polarizations may be important for future 5Gsystems where polarization selection may be different depending on theoperating frequency and the intended user. Therefore, the multi-beamantenna should perform sufficiently no matter the desired polarizationwith a minimum of 20 dB isolation between orthogonal polarizations.

In one embodiment, a half-wave dipole antenna receives a radio signal.The incoming radio wave (whose electric field is E) causes anoscillating electric current within the antenna elements, alternatelycharging the two sides of the antenna positively (+) and negatively (−).Since the antenna is one half a wavelength long at the radio wave'sfrequency, the voltage and current in the antenna form a standing wave.This oscillating current flows down the antenna's transmission linethrough the radio receiver (represented by resistor R).

FIG. 3 shows an exemplary simplified massive MIMO system with antennaports for user streams. Each user stream is a spatial stream of data.Each spatial stream that may include data from multiple users that areallocated different frequencies within the same spatial stream, in someembodiments. Further, a given user may be allocated multiple spatialstreams, in some embodiments. Therefore, the number of userscommunicating with the system may or may not correspond to the number ofantenna ports. In some embodiments, MIMO RX is configured to perform thefunctionality of channel estimator, MIMO detector, link qualityevaluator, etc. In some embodiments, MIMO TX is configured to performMIMO precoder.

During operation, a base station selects a number of antennas from amonga plurality of available antennas for use in MIMO wirelesscommunications. For example, the system may include 128 antennas but thebase station may select to use only 64 antennas during a given timeinterval based on current operating conditions. The decision of how manyantennas to use may be based on user input, a number of users currentlyin a cell, wireless signal conditions, bandwidth of currentcommunications, desired testing conditions, etc. The base station mayselect different numbers of antennas at different times, e.g., a largernumber during peak communications intervals and a smaller number duringtrough intervals. The base station determines a number of processingelements for processing received signals from the selected number ofantennas. In the illustrated embodiment, this is based on the number ofantennas selected and one or more threshold throughput values. In someembodiments, this determination may be based on any of variousappropriate parameters in addition to and/or in place of the parameters,including without limitation: the processing capacity of each processingelement, the amount of data per sample or entry for various information,a sampling rate, the number of spatial streams, number of users, etc.Determining the number of processing elements may include determining anumber of parallel receive chains for MIMO RX. In some embodiments, eachreceive chain includes a configurable MIMO core and a configurablelinear decoder. The base station processes incoming wirelesscommunications using the determined number of processing elements. Thismay include applying a MIMO signal estimation techniques such as MMSE,ZF, or MRC and decoding received data streams. After processing, thedecoded data from the determined number of processing elements may bereformatted and routed and transmitted to appropriate destinations(e.g., via another network such as a carrier network, the Internet,etc.). In some embodiments, the base station dynamically switchesbetween different MIMO signal estimation techniques, e.g., based on userinput, operating conditions, or any of various appropriate parameters.

The neural network control of the MIMO system may, in some embodiments,facilitate testing of MIMO base stations, reduce power consumptionduring MIMO communications, allow for flexibility in capacity, allow forflexibility in MIMO signal estimation, allow routing around defectiveprocessing elements or antennas, etc. In some embodiments, the basestation may also be dynamically or statically customized for a widevariety of operating conditions and/or research needs and may beconfigured for real-time processing.

The massive MIMO system may be included in base station, for example,and the TXRX data is provided to the neural network plane foroptimization. Data on the operation of any of the subunits of the MIMOsystem can be captured for learning system behavior and for optimizingthe system by the neural network or learning machine. In one embodiment,the subsystem includes front-end TX/RX units, antenna combiner, antennasplitter, bandwidth splitter, bandwidth combiner, channel estimator,MIMO detector, and MIMO precoder. Other subsystems of include additionalMIMO detectors, MIMO precoders, bandwidth splitters, and bandwidthcombiners. MIMO processing can be distributed among various processingelements. This may allow baseband processing to be partitioned acrossmultiple FPGAs, for example. This may facilitate scaling of massive MIMOsystems far beyond what a single centralized processing unit couldachieve for real-time baseband processing. For uplink symbols, eachTX/RX may be configured to digitize the received RF signals, performanalog front-end calibration and time/frequency synchronization, removethe cyclic prefix (CP), and perform FFT OFDM demodulation and guard-bandremoval. This may result in frequency domain pilot and unequalized datasymbol vectors, which is provided to antenna combiner. For downlinksymbols, each TX/RX may be configured to perform ODFM processing. Theantenna combiner, bandwidth splitter, MIMO precoder, bandwidth combiner,and antenna splitter are each located on a different SDR element thatalso implements one of TX/RXs. In one embodiment, channel estimator andMIMO detector are located on another SDR element that also implementsone of TX/RXs. In various embodiments, the various elements of FIG. 3may be partitioned among various hardware elements configured to performthe disclosed functionality. The hardware elements may be programmableand/or include dedicated circuitry. Antenna combiner is configured toreceive the yet unequalized OFDM symbols from each TX/RX and combinesthem into a signal sent to bandwidth splitter. This combines the signalsfrom up to N antennas in the subsystem. Combining this informationbefore further processing may allow the system to stay within throughputconstraints and may reduce the number of peer-to-peer connectionsbetween SDRs, for example. In some embodiments, the number of antennasfor which signals are combined by each antenna combiner is dynamicallyconfigurable. Bandwidth splitter is configured to split the receivedsignals into separate bandwidth portions and send the portions to MIMOdetectors in different subsystems. Thus, in the illustrated embodiment,processing is distributed across different processing elements that eachprocess data for a different frequency band. Each bandwidth portion mayinclude one or more subcarriers and the portions may or may not benon-overlapping. In some embodiments, the number of bandwidth portionsand the size of each portion is configurable, e.g., based on the numberof antennas, current number of users in communication, etc. In otherembodiments, processing may be distributed among processing elementsacross different time slices in addition to and/or in place of splittingby frequency. In some embodiments, bandwidth splitter is replaced with atime-slice splitter. Post-FTT subcarrier processing in OFDM may beinherently independent, allowing subsequent processing to be performedin parallel by different processing elements. The output of TX/RX can beprovided directly to bandwidth splitter and an output of bandwidthcombiner is provided directly to TX/RX. In other embodiments, theseoutputs may be provided to antenna combiner and antenna splittersimilarly to the other signals. In embodiments in which TX/RX andbandwidth splitter share the same SDR element and TX/RX and bandwidthcombiner share the same SDR element, however, the illustrated couplingmay conserve I/O resources. MIMO detector is configured to use anestimated channel matrix (e.g., based on uplink pilot symbols) to cancelinterference and detect frequency-domain symbols from each mobiledevice. As shown, in some embodiments MIMO detector is configured toprocess signals in a given bandwidth from multiple subsystems of system300. In the illustrated embodiment, MIMO detector is configured to sendthe detected signals to channel estimator and to link quality evaluator(included in a central controller in some embodiments) for furtherprocessing.

Channel estimator is configured to perform channel estimation for itsfrequency portion for a number of mobile devices, e.g., to producesoft-bits (also referred to as log-likelihood ratios (LLRs)) and providethem to link quality evaluator (coupling not shown). In someembodiments, multiple decoders are implemented, including a turbodecoder, for example. MIMO precoder is configured to receive downlinkdata from data source and precode the data based on channel estimates(e.g., estimated reciprocity calibration weights) from channelestimator. In some embodiments, the MIMO precoders are configured toperform precoding on different frequency portions of the downlink data.In some embodiments (not shown), the MIMO precoders in system 300 areconfigured to perform precoding on different time portions of thedownlink data. Bandwidth combiner is configured to combine signals atdifferent bandwidths from multiple MIMO precoders and send the data toantenna splitter. This may result in a complete set of precoded data fortransmission from the separately processed bandwidth portions. In otherembodiments, bandwidth combiner is configured to combine datacorresponding to separately-processed time slices in place of or inaddition to combining separately-processed frequency portions. Antennasplitter is configured to split the received signal and provide thesplit signal to TX/RXs for OFDM processing and transmission to mobiledevices or UEs. The set of antennas to which antenna splitter isconfigured to provide signals is dynamically configurable, in someembodiments (e.g., the number of antennas and/or the particular antennasin the set). Thus, in some embodiments, the set of processing elementsconfigured to perform distributed processing for particular antennasand/or users is dynamically configurable. Link quality evaluator isincluded in a central control unit and is configured to measure linkquality using one or more of various metrics such as bit error rate(BER), error vector magnitude (EVM), and/or packet-error rate (PER).

In various embodiments, the MIMO system is highly configurable, e.g.,based on user input and/or by the neural network based on traininghistory and current operating conditions. In some embodiments, variousdisclosed configuration operations are performed automatically. In someembodiments, the number of processing elements used at a given time toperform distributed processing for a set of users or a set of antennasis configurable. In some embodiments, the number of antennas used tocommunicate with each UE is configurable and/or dynamically determined.In some embodiments, the processing elements configured to performdifferent functionality described above is configurable. For example,the antenna combiner function may be moved from one FPGA to another FPGAor performed by multiple FPGAs. In some embodiments, the routing of databetween processing elements is configurable, e.g., to avoidmalfunctioning antennas and/or processing elements. In some embodiments,for example, system includes 16, 32, 64, 100, 128, 256, or moreantennas. In some embodiments, components of system are modular suchthat the number of antennas may be increased by adding additionalcomponents, and each antenna parameters can be captured and learned bythe neural network for subsequent optimization during live operation.

FIG. 4A shows an exemplary 5G control system that uses learning machinesor neural networks to improve performance. The neural network planeprovides automated intelligence to select the best operations givenparticular mobile device or wireless client needs. By enabling bothclient and infrastructure intelligence, the 5G networked system couldreason about the deficiencies it suffers from, and improve itsreliability, performance and security. By pushing more network knowledgeand functions to the end host, the 5G clients could play more activeroles in improving the user-experienced reliability, performance andsecurity. The neural plane sits above the data plane, control plane andmanagement plane. The Control Plane makes decisions about how to set upthe antenna settings and where traffic is sent. Control plane packetsare destined to or locally originated by the router itself. The controlplane functions include the system configuration, management, andexchange of routing table information. The route controller exchangesthe topology information with other routers and constructs a routingtable based on a routing protocol, for example, RIP, OSPF or BGP.Control plane packets are processed by the router to update the routingtable information. It is the signalling of the network. Since thecontrol functions are not performed on each arriving individual packet,they do not have a strict speed constraint and are less time-critical.The Data Plane or Forwarding Plane Forwards traffic to the next hopalong the path to the selected destination network according to controlplane logic. Data plane packets go through the router. Therouters/switches use what the control plane built to dispose of incomingand outgoing frames and packets. The management plane configures,monitors, and provides management, monitoring and configuration servicesto, all layers of the network stack and other parts of the system. Itshould be distinguished from the control plane, which is primarilyconcerned with routing table and forwarding information basecomputation.

On the client side, the system collect runtime, fine-grained information(protocol states, parameters, operation logic, etc.) from full-stackcellular protocols (physical/link layer, radio resource control,mobility management, data session management) inside the 5G device orphone, and such information is provided to the neural network plane. Oneembodiment extracts cellular operations from signaling messages betweenthe device and the network. These control-plane messages regulateessential utility functions of radio access, mobility management,security, data/voice service quality, to name a few. Given thesemessages, it further enables in-device analytics for cellular protocols.The system infers runtime protocol state machines and dynamics on thedevice side, but also infer protocol operation logic (e.g., handoffpolicy from the carrier) from the network. The system collects rawcellular logs from the cellular interface to the device user-space atruntime, and then parses them into protocol messages and extracts theircarried information elements. The parsed messages are then fed to theanalyzer which aims to unveil protocol dynamics and operation logics.Based on the observed messages and the anticipated behavior model (fromcellular domain knowledge), the analyzer infers protocol states,triggering conditions for state transitions, and protocol's takenactions. Moreover, it infers certain protocol operation logic (e.g.,handoff) that uses operator-defined policies and configurations. Itoffers built-in abstraction per protocol and allows for customize theseanalyzers. On the management plane, the system captures full-stacknetwork information on all-layer operations (from physical to datasession layer) over time and in space. This is achieved by crowdsourcingmassive network data from mobile devices temporally and spatially. Aninstability analyzer reports base station stability and reachability toavoid getting stuck in a suboptimal network. The instability analyzermodels the decision logic and feeds this model with real configurationscollected directly from the device and indirectly from the serving cell,as well as dynamic environment settings created for various scenarios.For example, antenna parameters (pointing direction, frequency, andRSSI/TSSI and channel) are captured to identify optimal settings for aparticular device/client. The system can model cellular protocols isderived from the 5G standards for each protocol. This works particularlywell for non-moving client devices such as 5G modems/routers and mobilephones that operate within a house or office most of the time, forexample. When the mobile device is on the move, population data can beused to optimize antenna and communication parameters to derive theoptimal connection for the device or client. For example, the neuralnetwork layer can identify clients using the Ultra Reliable Low LatencyCommunications specification (such as full car automation, factoryautomation, and remote-controlled surgery where reliability andresponsiveness are mandatory) and control the 5G network to respond toURLLC requests by delivering data so quickly and reliably thatresponsiveness will be imperceptibly fast by selecting appropriateantenna parameters and settings for URLLC from the tower to the clientdevice.

In addition to the neural network plane, the logical functionarchitecture includes a data plane, a control plane, and a managementplane. The control plane includes a software defined topology (SDT)logical entity configured to establish a virtual data-plane logicaltopology for a service, a software defined resource allocation (SDRA)logical entity configured to map the virtual data-plane topology to aphysical data-plane for transporting service-related traffic over thewireless network, and a software defined per-service customized dataplane process (SDP) logical entity configured to select transportprotocol(s) for transporting the service-related traffic over a physicaldata-plane of the wireless network. The management plane may includeentities for performing various management related tasks. For example,the management plane may include an infrastructure management entityadapted to manage spectrum sharing between different radio accessnetworks (RANs) and/or different wireless networks, e.g., wirelessnetworks maintained by different operators. The management plane mayalso include one or more of a data and analytics entity, a customerservice management entity, a connectivity management entity, and acontent service management entity, which are described in greater detailbelow.

The neural network plane works with network functions virtualization(NFV) to design, deploy, and manage networking services. It is acomplementary approach to software-defined networking (SDN) for networkmanagement. While SDN separates the control and forwarding planes tooffer a centralized view of the network, NFV primarily focuses onoptimizing the network services themselves. The neural network planeautomates the optimization level to the next automation and efficiency.

A virtual service specific serving gateway (v-s-SGW) can be done. Thev-s-SGW is assigned specifically to a service being provided by a groupof wirelessly enabled devices, and is responsible for aggregatingservice-related traffic communicated by the group of wirelessly enableddevices. In an embodiment, the v-s-SGW provides access protection forthe service-related traffic by encrypting/decrypting data communicatedover bearer channels extending between the v-s-SGW and thewirelessly-enabled devices. The v-s-SGW may also provide a layer two(L2) anchor point between the group of wirelessly-enabled devices. Forexample, the v-s-SGW may provide convergence between the differentwireless communication protocols used by the wirelessly-enabled devices,as well as between different wireless networks and/or RANs being accessby the wirelessly-enabled devices. Additionally, the v-s-SGW may performat least some application layer processing for the service relatedtraffic communicated by the wirelessly-enabled devices. Aspects of thisdisclosure further provide an embodiment device naming structure. Forthe v-s-SGW. Specifically, a v-s-SGW initiated on a network device isassigned a local v-u-SGW ID. Outgoing packets from the v-u-SGW IDinclude the local v-u-SGW ID and a host ID of the network device.Accordingly, recipients of those outgoing packets can learn the localv-u-SGW ID and the host ID associated with a particular v-s-SGW, andthereafter send packets to the v-s-SGW by including the local v-u-SGW IDand the host ID in the packet header.

Location tracking as a service (LTaaS) can be provided. The LTaaSfeature may track locations of user equipments (UEs) via a devicelocation tracking as a service (LTaaS) layer such that locations of theUEs are dynamically updated in a LTaaS layer as the UEs move todifferent locations in the wireless networks. In some embodiments, theLTaaS layer consists of a centralized control center. In otherembodiments, the LTaaS layer consists of a set of distributed controlcenters positioned in the wireless network, e.g., an applicationinstalled on a network device, such as a gateway or AP. In yet otherembodiments, the LTaaS layer comprises both a central controller centerand regional control centers. In such embodiments, the central controlcenter may be updated periodically by the regional control centers,which may monitor UE movement in their respective wireless networks. Inembodiments, the LTaaS layer may monitor general locations of the UEs.For example, the LTaaS layer may associate the UE's location with anetwork device in a specific wireless network, e.g., an access point, aserving gateway (SGW), etc.

Content may be cached in network devices of wireless network or radioaccess network (RAN) in anticipation that a mobile device or user willwant to access the content in the future. In some embodiments, a contentforwarding service manager (CFM) may select content to be pushed to acaching location in the wireless network based on the popularity ofavailable content stored in one or more application servers. The networkdevice may comprise a virtual information-centric networking (ICN)server of an ICN virtual network (VN), and may be adapted to provide thecached content to a virtual user-specific serving gateway (v-u-SGW) of aserved user equipment (UE) upon request. Notably, the cached content isstored by the network device in an information-centric networking (ICN)format, and the v-u-SGW may translate the cached content from the ICNformat to a user-specific format upon receiving the cached contentpursuant to a content request. The v-u-SGW may then relay the cachedcontent having the user-specific format to a served UE. After thecontent is pushed to the network device, the content forwarding servicemanager (CFM) may update a content cache table to indicate that thecontent has been cached at the network device. The content cache tablemay associate a name of the content with a network address of thenetwork device or the virtual IVN server included in the network device.The ICN VN may be transparent to the served UE, and may be operated byone of the wireless network operators or a third party. These and otheraspects are described in greater detail below.

The management plane 310 may include entities for performing variousmanagement related tasks. In this example, the management plane 330includes a data and analytics entity 311, an infrastructure managemententity 312, customer service management entity 313, a connectivitymanagement entity 314, and a content service management entity 315. Thedata and analytics entity 311 is configured to provide data analytics asa service (DAaaS). This may include manage on-demand network statusanalytics and on-demand service QoE status analytics for a particularservice, and providing a data analytics summary to a client. Theinfrastructure management entity 312 may manage spectrum sharing betweendifferent radio access network (RANs) in a wireless network, or betweenwireless networks maintained by different operators. This may includewireless network integration, management of RAN backhaul and access linkresources, coordination of spectrum sharing among co-located wirelessnetworks, access management, air interface management, and device accessnaming and network node naming responsibilities.

The customer service management entity 313 may provide customer servicefunctions, including managing customer context information,service-specific quality of experience (QoE) monitoring, and chargingresponsibilities. The connectivity management entity 314 may providelocation tracking as a service (LTaaS) over the data plane of thewireless network. The connectivity management entity 314 may also haveother responsibilities, such as establishing customized and scenarioaware location tracking scheme, establishing software defined andvirtual per-mobile user geographic location tracking schemes, andtriggering user specific data plane topology updates. The contentservice management entity 315 may manage content caching in the wirelessnetwork. This may include selecting content to be cached in RAN,selecting caching locations, configuring cache capable network nodes,and managing content forwarding. In some embodiments, the managementplane may also include a security management entity that is responsiblefor network access security (e.g., service-specific security, customerdevice network access protection, etc.), as well as inter-domain andintra-domain wireless network security.

The control plane 320 may include entities for performing variouscontrol related tasks. In this example, the control plane includes asoftware defined topology (SDT) logical entity 322, a software definedresource allocation (SDRA) logical entity 324, and a software definedper-service customized data plane process (SDP) logical entity 326. TheSDT entity 322, the SDRA logical entity 324, and the SDP logical entity326 may collectively configure a service-specific data plane forcarrying service-related traffic. More specifically, the softwaredefined topology (SDT) logical entity 322 is configured to establish avirtual data-plane logical topology for a service. This may includeselecting network devices to provide the service from a collection ofnetwork devices forming the data plane 330. The software definedresource allocation (SDRA) logical entity 324 is configured to map thevirtual data-plane topology to a physical data-plane for transportingservice-related traffic over the wireless network. This may includemapping logical links of the virtual data-plane topology to physicalpaths of the data plane. The software defined per-service customizeddata plane process (SDP) logical entity 326 is configured to selecttransport protocol(s) for transporting the service-related traffic overa physical data-plane of the wireless network. The transport protocolsmay be selected based on various criteria. In one example, the SDPlogical entity selects the transport protocol based on a characteristicof the service-related traffic, e.g., business characteristic, payloadvolume, quality of service (QoS) requirement, etc. In another example,the SDP logical entity selects the transport protocol based on acondition on the network, e.g., loading on the data paths, etc.

The SDT entity 322, the SDRA logical entity 324, and the SDP logicalentity 326 communicate with the neural network plane to optimize thesystem configuration (including antenna pointing/setting/redundancyassignment, among others), and they may also have other responsibilitiesbeyond their respective roles in establishing a service-specific dataplane. For example, the SDT entity 322 may dynamically define keyfunctionality for v-s-SGWs/v-u-SGWs, as well as enable mobile VNmigration and provide mobility management services. As another example,the SDRA logical entity 324 may embed virtual network sessions, as wellas provide radio transmission coordination. One or both of the SDTentity 322 and the SDRA logical entity 324 may provide policy andcharging rule function (PCRF) services.

The SDT entity 322, the SDRA logical entity 324, and the SDP logicalentity 326 may collectively configure a service-specific data plane forcarrying service-related traffic. Specifically, the SDT entity 322establishes a virtual data-plane logical topology for the service, theSDRA logical entity 324 maps the virtual data-plane topology to aphysical data-plane path for transporting service-related traffic overthe wireless network, and the SDP logical entity 326 select transportprotocol(s) for transporting the service-related traffic over thephysical data-plane.

In one example, the neural network can automatically allocate functionsin a mobile network based at least in part on utilization levels. Forexample, various components of the 5G network can include, but are notlimited to, a network exposure function (NEF), a network resourcefunction (NRF), an authentication server function (AUSF), an access andmobility management function (AMF), a policy control function (PCF), asession management function (SMF), a unified data management (UDM)function, a user plane function (UPF), and/or an application function(AF). For example, some or all of the functions discussed herein canprovide utilization levels, capability information, localityinformation, etc., associated with the various functions to a networkresource function (NRF) (or other component), for example, such that theNRF or other component can select a particular function of a pluralityof possible components providing the same function based on theutilization levels of the particular component. Thus, the system,devices, and techniques broadly apply to selecting network functions,and is not limited to a particular context or function, as discussedherein.

The neural network plane improves the functioning of a network by takinga global management view to optimize the network by reducing networkcongestion, dropped packets, or dropped calls due to overutilization ofresources. Further, the systems, devices, and techniques can reduce asize of components (e.g., processing capacity) by obviating or reducingany need to over-allocate resources to ensure spare capacity to reducecongestion. Further, selecting functions based on utilization levels canreduce signaling overhead associated with dynamically allocating a sizeof a virtual instance. In some instances, the architecture describedherein facilitates scalability to allow for additional components to beadded or removed while maintaining network performance. In someinstances, optimal functions can be selected in connection withhandovers (e.g., intracell or intercell) to balance a load on networkfunctions to provide improved Quality of Service (QoS) for networkcommunications. These and other improvements to the functioning of acomputer and network are discussed herein.

In one example, the neural network plane interacts with a user equipment(UE), an access and mobility management function (AMF), a networkresource function (NRF), a session management function (SMF), and a userplane function (UPF). The UE can transmit a registration request to theAMF. At a same or different time as the registration request, the UPFcan transmit utilization information to the NRF, which in turncommunicates with the neural network plane. In some instances, theutilization information can include information including, but notlimited to: CPU utilization level; memory utilization level; active orreserved bandwidth; a number of active sessions; a number of allowablesessions; historical usage; instantaneous usage; dropped packets; packetqueue size; delay; Quality of Service (QoS) level, antenna efficiency,antenna setting; and the like. Further, the utilization information caninclude a status of the UPF (e.g., online, offline, schedule formaintenance, etc.). In some instances, the UPF can transmit theutilization info at any regular or irregular interval. In someinstances, the UPF can transmit the utilization info in response to arequest from the NRF, and/or in response to a change in one or moreutilization levels above or below a threshold value.

Next, the UE can transmit a session request to the AMF, which in turncan transmit the session request to the SMF. In some instances, thesession request can include a request to initiate a voice communication,a video communication, a data communication, and the like, by andbetween the UE and other services or devices in the network. The SMF inturn talks to the neural network plane for management. Based on itslearned optimization, the neural network plane communicates instructionsto the SMF. At least partially in response to receiving command from theneural network plane, the SMF can transmit a UPF query to the NRF. Insome instances, the UPF query can include information including, but notlimited to: a type of session requested by the UE (e.g., voice, video,bandwidth, emergency, etc.); services requested by the UE; a location ofthe UE; a location of a destination of the session requested by the UE;a request for a single UPF or a plurality of UPFs; and the like.

In some instances, at least partially in response to receiving the UPFquery, the NRF can provide a UPF response to the SMF. In some instances,the UPF response can include one or more identifiers associated with oneor more UPFs that are available to provide services to the UE. In someinstances, the UPF response can be based at least in part on the sessionrequest and/or on the utilization info received from the UPF (as well asother UPFs, as discussed herein).

Based at least in part on the UPF response, the SMF can select a UPF(e.g., in a case where a plurality of UPF identifiers are provided tothe SMF) or can utilize the UPF provided by the NRF for a communicationsession. The SMF can select a UPF and can transmit a UPF selection tothe UPF that has been selected and/or designated to providecommunications to the UE.

At least partially in response to the UPF selection, the UPF can provideservices to the UE. As discussed herein, the UPF can facilitate datatransfer to and/or from the UE to facilitate communications such asvoice communications, video communications, data communications, etc.

In this manner, the neural network plane incorporates intelligence inproviding services to requests in a way that optimizes system hardwareand software resources and overall cost.

Next, an example process is disclosed for selecting a network function,such as a user plane function, based on utilization information learnedby the neural network. The example process can be performed by theneural network in conjunction with the network resource function (NRF)(or another component), in connection with other components discussedherein. First, the neural network receives utilization informationassociated with one or more network functions, such as one or more userplanes. Although discussed in the context of a UPF, this process applyequally to other network functions, such as a network exposure function(NEF), a policy control function (PCF), a unified data management (UDM),an authentication server function (AUSF), an access and mobilitymanagement function (AMF), a session management function (SMF), anapplication function (AF), and the like. In one example, user planes ina network can transmit utilization information to the NRF. In someinstances, the NRF can request utilization information from various UPFs(or any network function) on a regular schedule, upon receipt of arequest to initiate a communication, and then forwarding suchinformation to the neural network plane for training, for example. Insome instances, the UPF (or any network function) can transmitutilization information upon determining that a utilization level haschanged more than a threshold amount compared to a previous utilizationlevel. In some instances, utilization information can include, but isnot limited to, one or more of: CPU utilization (e.g., % utilization),bandwidth utilization, memory utilization, number of allowable sessions,number of active sessions, historical utilization information, expectedutilization levels, latency, current QoS of active sessions, and thelike. Further, in some instances, the neural network can receivecapability information associated with the user plane(s) (or any networkfunction), location information associated with the user plane(s) (orany network function), etc. Such utilization information, capabilityinformation, location information, etc. can be stored in a databaseaccessible by the NRF.

Next, the process can include receiving a request for a networkfunction, such as a user plane, the request associated with a userequipment. For example, a request can be received from a sessionmanagement function (SMF) or an access and mobility management function(AMF) (or any network function) for a user plane (or any networkfunction) to initiate a communication for a user equipment. In someinstances, the request can indicate a number of user planes (or anynetwork function) to be provided by the NRF (e.g., one or many). In someinstances, the request can include information associated with thecommunication, such as a type of the communication, locations of the UEand/or the destination of the communication, specialized services (e.g.,video encoding, encryption, etc.) requested in association with thecommunication, a bandwidth of the communication, a minimum QoS of thecommunication, and the like. In some instances, the request can be basedat least in part on a request initiated by the UE and provided to theAMF, the SMF, or any network function.

Operations by the neural network plane includes determining one or morenetwork functions (e.g., user planes) based at least in part on therequest and the utilization level. For example, the neural network planecan include determining that a first user plane (or any networkfunction) is associated with a first utilization level (e.g., 80% CPUutilization) and a second user plane (or any network function) isassociated with a second utilization level (e.g., 30% utilizationlevel). Further the neural network can include determining that thefirst utilization level is above a utilization threshold (e.g., 70% orany value) such that addition assignments of UEs to the UPF (or anynetwork function) may degrade a quality of connections associated withfirst UPF (or any network function). Accordingly, the neural network candetermine that the first UPF (or any network function) is to be selectedto provide data traffic for the U E.

As can be understood herein, there may be a variety of learningalgorithms or ways to determine which user planes (or any networkfunction) are to be selected as available for a communication. In someinstances, the neural network can include determining that theutilization level of the second user plane (or any network function)(e.g., 30%, discussed above) is lower than the utilization level of thefirst user plane (or any network function) (e.g., 80%, discussed above),and accordingly, can determine that the second user plane (or anynetwork function) is to be selected for the communication.

The neural network determines a plurality of user planes (or any networkfunction) that are available for a communication (e.g., that have autilization level below a threshold value). In some instances, the userplanes (or any network function) can be selected based on a proximity tothe UE, capabilities requested by the UE, etc. In some instances, theoperation 506 can include ranking or prioritizing individual ones of theplurality of user planes (or any network function) as most appropriateto be selected for the communication. The neural network then providesan identification of the one or more user planes (or any networkfunction) to a session management function (SMF) (or any selectingnetwork function) to facilitate a communication with the user equipment.For example, the operation by the neural network can include providingan address or other identifier corresponding to one or more UPFs (or anyone or more network functions) to an SMF (or any selecting networkfunction) in the network. In the case where one user plane (or anynetwork function) is provided, the SMF (or any selecting networkfunction) may utilize the explicit user plane (or any network function)identified by the NRF. In the case where more than one user plane (orany network function) is provided, the identification may includeadditional information to allow the SMF (or any selecting networkfunction) to select a user plane (or any network function), as discussedherein.

In another example for selecting a user plane function based onutilization information during a handover performed by the neuralnetwork (or another component), in connection with other componentsdiscussed herein. As usual, the neural network has utilizationinformation associated with one or more user planes which provideutilization information to NRF that in turn sends the info to the neuralnetwork layer. Upon receiving a request for a user plane, the neuralnetwork plane can include providing a first selection of at least onefirst user plane based at least in part on the request and theutilization information. The operation can include the providing,allocating, and/or selecting at least one user plane based onutilization information to balance a load across a plurality ofavailable user planes. In some instances, the operation 606 can includeestablishing a communication for the UE at a first radio access network(RAN) utilizing the first user plane. The neural network can receive anindication of a handover request. For example, as a UE moves about anenvironment, a signal quality can decrease between the UE and the firstRAN. Accordingly, the neural network can automatically change antennaparameters first based on learned parameters, and if that does notchange signal quality, the neural network can determine that a handovershould occur, based on one or more of, but not limited to: signalstrength of an anchor connection (e.g., a signal strength of the firstRAN); signal strength of a target RAN (e.g., a signal strength of asecond RAN); latency; UE speed/direction; traffic level(s); QoS; etc. Insome instances, the neural network determines that a new user plane isrequired/desired based at least in part on the indication of thehandover request. The neural network plane can provide a secondselection of at least one second user plane based at least in part onthe handover request and the utilization information. For example, theat least one second user plane can include user planes suitable andavailable to facilitate a communication with the UE. In some instances,the above operations can be repeated as a UE moves about an environment(and/or in response to initiate a handover based on UPF maintenance, forexample). That is, the operations can be repeated continuously orperiodically to determine a user plane to facilitate a communicationwhile balancing a load of the user planes.

The neural network plane can automatically configure the direction ofantennas and combine antennas in a massive MIMO antenna by firstfocusing the antenna on the UE device (which optimizes thedirectionality of the wireless link between the BS and the UE), and thentransmitting first pilot signals via each of multiple antennas of theUE; receiving antenna combining information from a base station (BS),the antenna combining information for combining the multiple antennasinto one or more antenna groups and an orthogonal sequence allocated toeach of the one or more antenna groups; and transmitting second pilotsignals to the BS using the allocated orthogonal sequences, wherein thesecond pilot signals are used for estimating downlink channels from theBS to the UE, wherein the antenna combining information is determinedbased on correlation of each of the multiple antennas obtained from thefirst pilot signals, and wherein a same orthogonal sequence is appliedto a second pilot signal transmitted via one or more of the multipleantennas belonging to a same antenna group. The neural network can senda preferred antenna combination that is sent to the BS based on one ormore of the following: 1) minimize a correlation between effectivechannels of the one or more antenna groups, 2) an amount of data to betransmitted, 3) second pilot signals. The second pilot signals can becaptured during different time periods than a time period during which aUE of belonging to a second UE group transmits the second pilot signals.The 1st pilot signal can be transmitted by the UE even after the UEconfigure the antenna combination. In this case, the base station mayconfigure new antenna combination based on the previous antennacombination (mapping between one logical channel and another logicalchannel). Based on this, the base station may determine antennacombining information and transmit it to the UE and to make each of thelogical (effective) channels become orthogonal to each other. The neuralnetwork plane monitors performance and can automatically reconfigure ormodify antenna combination when the SINR of the received signals becomepoor over a predetermined period of time. Based on this request, thebase station may receive the antenna combining information again andtransmit it to the UE. The neural network plane may determine theantenna combining information to minimize the biggest correlation valuebetween the effective channels. Or, it may determine to make the biggestcorrelation value between the effective channels less than a thresholdvalue. By doing this, the base station may prevent the antenna groupsfrom being aligned in the same direction. In another example, supposethere are 2 UEs (UE a and UE b) and that the UE a has lots of data to betransmitted/received while there are little for UE b. In this case, theneural network provides more effective channels to UE a while UE b getsfewer number of effective channels. In another example, the UE maydetermine the preferred antenna combining method based on the ACK/NACKof the received data. When the number of effective channels increases,the more diversity gain can be acquired. So, the UE of this examplerequest more number of effective channels when the decoding results ofthe received data is NACK for certain number of time. Otherwise, the UEmay request less number of effective channels. In still another example,the UE may determine the preferred antenna combining method based on theestimated channel information. The above preferred antenna combiningmethods of the UE can be controlled and granted by the network. Theneural network may consider not only the UE transmitted this preferredantenna combining method, but other UEs within the cell.

In one implementation, FIG. 4B shows an exemplary learning machine toautomatically adjust the position/aim of the antennas to optimize datatransmission performance and/or coverage. As noted earlier, 4G systemshave range but lack speed. 5G systems have speed but requires moreantennas and generally lacks the range of 4G systems. To optimizeperformance, a learning machine is used to automatically track a mobiledevice and adjust the best arrangement for the antenna arrays. Theprocess is as follows:

-   -   Collect performance data from subsystems (see above) such as:        Spatial and Modulation Symbols, RSSI, TSSI, CSI (channel state        information), and attributes on channel matrix and error vector        magnitude, for example    -   Extract features and train learning machine to optimize spectral        efficiency and energy efficiency of the wireless system    -   During live communication, extract features from live 5G data        and select antenna orientation/setting/params based on client        device, resources available, and tower network properties for        optimum transmission.

FIGS. 7C-7D show exemplary learning machine details. While the learningmachine optimizes all resources, details on the antenna are discussednext, with the expectation that other resource allocations The learningmachine turn the antenna arrays “smart” so that the best antenna linkagebetween transceivers is achieved. Further, when one of the antennaelements in the array fails, the beamforming and beamsteeringperformance of the array degrades gracefully. Such an objective isachieved by reconfiguring the array when an element is found to bedefective, by either changing the material properties of the substrateor by applying appropriate loading in order to make the array functionalagain. One embodiment changes the excitation coefficient for each arrayelement (magnitude and phase) to optimize for changes due to theenvironment surrounding an array antenna. Using learning machines, onecan train the antenna array to change its elements' phase or excitationdistribution in order to maintain a certain radiation pattern or toenhance its beamsteering and nulling properties and solve the directionof arrival (DOA) as well.

The neural network control of the MIMO antennas provides significantgains that offer the ability to accommodate more users, at higher datarates, with better reliability, while consuming less power. Using neuralnetwork control of large number of antenna elements reduces power in agiven channel by focusing the energy to targeted mobile users usingprecoding techniques. By directing the wireless energy to specificusers, the power in channel is reduced and, at the same time,interference to other users is decreased.

In addition to controlling the 5G operation, the neural network can beused to provide local edge processing for IOT devices. A strikingfeature about neural networks is their enormous size. To reduce size ofthe neural networks for edge learning while maintaining accuracy, thelocal neural network performs late down-sampling and filter countreduction, to get high performance at a low parameter count. Layers canbe removed or added to optimize the parameter efficiency of the network.In certain embodiments, the system can prune neurons to save some space,and a 50% reduction in network size has been done while retaining 97% ofthe accuracy. Further, edge devices on the other hand can be designed towork on 8 bit values, or less. Reducing precision can significantlyreduce the model size. For instance, reducing a 32 bit model to 8 bitmodel reduces model size. Since DRAM memory access is energy intensiveand slow, one embodiment keeps a small set of register files (about 1KB) to store local data that can be shared with 4 MACs as the leaningelements). Moreover, for video processing, frame image compression andsparsity in the graph and linear solver can be used to reduce the sizeof the local memory to avoid going to off chip DRAMs. For example, thelinear solver can use a non-zero Hessian memory array with a Choleskymodule as a linear solver.

In one embodiment, graphical processors (GPUs) can be used to domultiply-add operations in neural networks. In another embodiment, in aTensor processing unit (TPU), a systolic array can be used to do themultiply-add operations. The matrix multiplication reuses both inputsmany times as part of producing the output. The neural processor canread each input value once, but use it for many different operationswithout storing it back to a register. Wires only connect spatiallyadjacent ALUs, which makes them short and energy-efficient. The ALUsperform only multiplications and additions in fixed patterns, whichsimplifies their design. The systolic array chains multiple ALUstogether, reusing the result of reading a single register. During theexecution of this massive matrix multiply, all intermediate results arepassed directly between 64K ALUs without any memory access,significantly reducing power consumption and increasing throughput.

In another embodiment, original full neural network can be trained inthe cloud, and distillation is used for teaching smaller networks usinga larger “teacher” network. Combined with transfer learning, this methodcan reduce model size without losing much accuracy. In one embodiment,the learning machine is supported by a GPU on a microprocessor, or toreconfigure the FPGA used as part of the baseband processing as neuralnetwork hardware.

The system can implement Convolutional Neural Networks (CNN) such asAlexNet with 5 Convolutional Layers and 3 Fully Connected Layers.Multiple Convolutional Kernels (a.k.a filters) extract interestingfeatures in an image. In a single convolutional layer, there are usuallymany kernels of the same size. For example, the first Conv Layer ofAlexNet contains 96 kernels of size 11×11×3. Note the width and heightof the kernel are usually the same and the depth is the same as thenumber of channels. The first two Convolutional layers are followed bythe Overlapping Max Pooling layers that we describe next. The third,fourth and fifth convolutional layers are connected directly. The fifthconvolutional layer is followed by an Overlapping Max Pooling layer, theoutput of which goes into a series of two fully connected layers. Thesecond fully connected layer feeds into a softmax classifier with 1000class labels. ReLU nonlinearity is applied after all the convolution andfully connected layers. The ReLU nonlinearity of the first and secondconvolution layers are followed by a local normalization step beforedoing pooling. But researchers later didn't find normalization veryuseful. So we will not go in detail over that. Max Pooling layers areusually used to downsample the width and height of the tensors, keepingthe depth same. Overlapping Max Pool layers are similar to the Max Poollayers, except the adjacent windows over which the max is computedoverlap each other. The authors used pooling windows of size 3×3 with astride of 2 between the adjacent windows. This overlapping nature ofpooling helped reduce the top-1 error rate by 0.4% and top-5 error rateby 0.3% respectively when compared to using non-overlapping poolingwindows of size 2×2 with a stride of 2 that would give same outputdimensions.

It should also be appreciated that, while the antenna system of thepresent invention is primarily intended for 5G/6G systems, it can beused in space-borne communication applications, radar, as well as otherterrestrial applications, or in any application requiring a large,lightweight, stowable antenna.

If it is said that an element “A” is coupled to or with element “B,”element A may be directly coupled to element B or be indirectly coupledthrough, for example, element C. When the specification or claims statethat a component, feature, structure, process, or characteristic A“causes” a component, feature, structure, process, or characteristic B,it means that “A” is at least a partial cause of “B” but that there mayalso be at least one other component, feature, structure, process, orcharacteristic that assists in causing “B.” If the specificationindicates that a component, feature, structure, process, orcharacteristic “may”, “might”, or “could” be included, that particularcomponent, feature, structure, process, or characteristic is notrequired to be included. If the specification or claim refers to “a” or“an” element, this does not mean there is only one of the describedelements.

An embodiment is an implementation or example. Reference in thespecification to “an embodiment,” “one embodiment,” “some embodiments,”or “other embodiments” means that a particular feature, structure, orcharacteristic described in connection with the embodiments is includedin at least some embodiments, but not necessarily all embodiments. Thevarious appearances of “an embodiment,” “one embodiment,” or “someembodiments” are not necessarily all referring to the same embodiments.It should be appreciated that in the foregoing description of exemplaryembodiments, various features are sometimes grouped together in a singleembodiment, figure, or description thereof for the purpose ofstreamlining the disclosure and aiding in the understanding of one ormore of the various novel aspects. This method of disclosure, however,is not to be interpreted as reflecting an intention that the claimedembodiments requires more features than are expressly recited in eachclaim. Rather, as the following claims reflect, novel aspects lie inless than all features of a single foregoing disclosed embodiment. Thus,the claims are hereby expressly incorporated into this description, witheach claim standing on its own as a separate embodiment.

Many of the methods are described in their most basic form, butprocesses can be added to or deleted from any of the methods andinformation can be added or subtracted from any of the above descriptionwithout departing from the basic scope of the present embodiments. Itwill be apparent to those skilled in the art that many furthermodifications and adaptations can be made. The particular embodimentsare not provided to limit the concept but to illustrate it. The scope ofthe embodiments is not to be determined by the specific examplesprovided above but only by the claims below.

What is claimed is:
 1. A system, comprising: a distributed ledger orblockchain storing one or more smart contracts; one or more 5G smallcells, each having one or more antennas mounted on a housing, each smallcell sending packets of data trackable with the distributed ledger; anda processor coupled to the antennas in communication with apredetermined target using 5G protocols, the processor accessing a smartcontract stored on the distributed ledger or blockchain, the smartcontract specifying conditions for payment to a host providing wirelessnetwork coverage in a cryptographically verified physical location andtime as part of 5G cellular network providers without a singlecoordinator.
 2. The system of claim 1, wherein the processor calibratesa radio link between a transceiver in the housing and a client device,wherein the client device pays to communicate data and to geolocate,wherein the host or processor receives fees or tokens for providingnetwork coverage and for validating network integrity.
 3. The system ofclaim 1, wherein the processor is coupled to fiber optics cable tocommunicate with a cloud-based radio access network (RAN) or a remoteRAN.
 4. The system of claim 1, comprising one or more devices in the 5Gnetwork to send and receive encrypted data from the Internet wherefingerprints of the data sent are stored in the blockchain and wheredevices spend fees by paying hosts to send data to and from theInternet.
 5. The system of claim 1, comprising routers that receivepackets from devices via hotspots and route packets to predetermineddestinations and provide one of: authentication, routing packets fromhotspots and routing them to the Internet, providing deliveryconfirmations to ensure transport transactions are honest, and providinga full copy of the blockchain ledger by acting as a full node.
 6. Thesystem of claim 5, wherein the smart contract specifies predeterminedrules and tokens to pay the small cell in exchange for network access.7. The system of claim 1, wherein the processor controls actuatorscoupled to the antennas.
 8. The system of claim 1, wherein thedistributed ledger stores location data of the small cell and thepredetermined target obtains the location of the small cell.
 9. Thesystem of claim 1, wherein certainty of location data is increased byestablishing a multidirectional proof of location by having multiplenearby wireless nodes validate an occurrence and range of an interactionby a client node by cosigning the interaction to provide azero-knowledge proof that the two nodes were in proximity of each other,wherein interactions on the blockchain by an edge produce the BestAnswer from the relative proximity of all the nodes that are in thenetwork.
 10. The system of claim 1, wherein processor, given a set ofreported data and a query for a relative position of one of a pluralityof edge nodes, generates an approximation of a position and stores theapproximation on the ledger or blockchain.
 11. The system of claim 1,comprising a neural network coupled to a control plane, a managementplane, and a data plane to optimize 5G parameters.
 12. The system ofclaim 1, comprising one or more cameras and sensors in the housing tocapture security information.
 13. The system of claim 1, wherein thehousing is a drop in replacement for an existing housing.
 14. The systemof claim 1, comprising routers and client devices that record to theblockchain predetermined routers passing data to a host, such that atransmitter on a network sends device data to a selected router at apredetermined destination and the router confirms with the transmitterthat device data is received at a predetermined destination and a hostaccount is credited.
 15. The system of claim 1, comprising an edgelearning machine in the housing to provide local edge processing forInternet-of-Things (IOT) sensors.
 16. The system of claim 1, comprisingpre-trained models and modifies the pre-trained models for a selectedtask.
 17. The system of claim 1, comprising a smart contract for thepredetermined target to access a learning machine.
 18. The system ofclaim 1, comprising a cloud trained neural network whose networkparameters are reduced before transferring to the edge neural network.19. A system, comprising: a distributed ledger or blockchain storing oneor more smart contracts; one or more 5G small cells, each having one ormore antennas mounted on a housing, each small cell sending packets ofdata trackable with the distributed ledger; and a processor coupled tothe antennas in communication with a predetermined target using 5Gprotocols, the processor accessing a smart contract stored on thedistributed ledger or blockchain, the smart contract verifying physicallocation and time as part of 5G cellular network without a singlecoordinator.
 20. The system of claim 19, wherein certainty of locationdata is increased by establishing a multidirectional proof of locationby having multiple nearby wireless nodes validate an occurrence andrange of an interaction by a client node by cosigning the interaction toprovide a zero-knowledge proof that the two nodes are in proximity ofeach other.
 21. A system, comprising: a distributed ledger or blockchainstoring one or more smart contracts; one or more 5G small cells, eachhaving one or more antennas mounted on a housing, each small cellsending packets of data trackable with the distributed ledger; and aprocessor coupled to the antennas in communication with a predeterminedtarget using 5G protocols, the processor accessing a smart contractstored on the distributed ledger or blockchain, the smart contractverifying physical location as part of 5G cellular network without asingle coordinator.
 22. The system of claim 21, wherein certainty oflocation data is increased by establishing a multidirectional proof oflocation by having multiple nearby wireless nodes validate an occurrenceand range of an interaction by a client node by cosigning theinteraction to provide a zero-knowledge proof that the two nodes are inproximity of each other.